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Tran-Canh, Dung. "Simulating the flow of some non-Newtonian fluids with neural-like networks and stochastic processes." University of Southern Queensland, Faculty of Engineering and Surveying, 2004. http://eprints.usq.edu.au/archive/00001518/.
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The thesis reports a contribution to the development of neural-like network- based element-free methods for the numerical simulation of some non-Newtonian fluid flow problems. The numerical approximation of functions and solution of the governing partial differential equations are mainly based on radial basis function networks. The resultant micro-macroscopic approaches do not require any element-based discretisation and only rely on a set of unstructured collocation points and hence are truly meshless or element-free. The development of the present methods begins with the use of the multi-layer perceptron networks (MLPNs) and radial basis function networks (RBFNs) to effectively eliminate the volume integrals in the integral formulation of fluid flow problems. An adaptive velocity gradient domain decomposition (AVGDD) scheme is incorporated into the computational algorithm. As a result, an improved feed forward neural network boundary-element-only method (FFNN- BEM) is created and verified. The present FFNN-BEM successfully simulates the flow of several Generalised Newtonian Fluids (GNFs), including the Carreau, Power-law and Cross models. To the best of the author's knowledge, the present FFNN-BEM is the first to achieve convergence for difficult flow situations when the power-law indices are very small (as small as 0.2). Although some elements are still used to discretise the governing equations, but only on the boundary of the analysis domain, the experience gained in the development of element-free approximation in the domain provides valuable skills for the progress towards an element-free approach. A least squares collocation RBFN-based mesh-free method is then developed for solving the governing PDEs. This method is coupled with the stochastic simulation technique (SST), forming the mesoscopic approach for analyzing viscoelastic flid flows. The velocity field is computed from the RBFN-based mesh-free method (macroscopic component) and the stress is determined by the SST (microscopic component). Thus the SST removes a limitation in traditional macroscopic approaches since closed form constitutive equations are not necessary in the SST. In this mesh-free method, each of the unknowns in the conservation equations is represented by a linear combination of weighted radial basis functions and hence the unknowns are converted from physical variables (e.g. velocity, stresses, etc) into network weights through the application of the general linear least squares principle and point collocation procedure. Depending on the type of RBFs used, a number of parameters will influence the performance of the method. These parameters include the centres in the case of thin plate spline RBFNs (TPS-RBFNs), and the centres and the widths in the case of multi-quadric RBFNs (MQ-RBFNs). A further improvement of the approach is achieved when the Eulerian SST is formulated via Brownian configuration fields (BCF) in place of the Lagrangian SST. The SST is made more efficient with the inclusion of the control variate variance reduction scheme, which allows for a reduction of the number of dumbbells used to model the fluid. A highly parallelised algorithm, at both macro and micro levels, incorporating a domain decomposition technique, is implemented to handle larger problems. The approach is verified and used to simulate the flow of several model dilute polymeric fluids (the Hookean, FENE and FENE-P models) in simple as well as non-trivial geometries, including shear flows (transient Couette, Poiseuille flows)), elongational flows (4:1 and 10:1 abrupt contraction flows) and lid-driven cavity flows.
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Sze, Tiam Lin. "System identification using radial basis function networks." Thesis, University of Sheffield, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.364232.
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Zhao, Yan. "Cervical cell classification with radial basis function networks." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp01/MQ27559.pdf.
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Howell, Andrew Jonathan. "Automatic face recognition using radial basis function networks." Thesis, University of Sussex, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.241635.
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Shahsavand, Akbar. "Optimal and adaptive radial basis function neural networks." Thesis, University of Surrey, 2000. http://epubs.surrey.ac.uk/844452/.
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The optimisation and adaptation of single hidden layer feed-forward neural networks employing radial basis activation functions (RBFNs) was investigated. Previous work on RBFNs has mainly focused on problems with large data sets. The training algorithms developed with large data sets prove unreliable for problems with a small number of observations, a situation frequently encountered in process engineering. The primary objective of this study was the development of efficient and reliable learning algorithms for the training of RJBFNs with small and noisy data sets. It was demonstrated that regularisation is essential in order to filter out the noise and prevent over-fitting. The selection of the appropriate level of regularisation, lambda*, with small data sets presents a major challenge. The leave-one-out cross validation technique was considered as a potential means for automatic selection of lambda*. The computational burden of selecting lambda* was significantly reduced by a novel application of the generalised singular value decomposition. The exact solution of the multivariate linear regularisation problem can be represented as a single hidden layer neural network, the Regularisation Network, with one neurone for each distinct exemplar. A new formula was developed for automatic selection of the regularisation level for a Regularisation Network with given non-linearities. It was shown that the performance of a Regularisation Network is critically dependent on the non-linear parameters of the activation function employed; a point which has received surprisingly little attention. It was demonstrated that a measure of the effective degrees of freedom df(lambda*,alpha) of a Regularisation Network can be used to select the appropriate width of the local radial basis functions, alpha, based on the data alone. The one-to-one correspondence between the number of exemplars and the number of hidden neurones of a Regularisation Network may prove computationally prohibitive. The remedy is to use a network with a smaller number of neurones, the Generalised Radial Basis Function Network (GRBFN). The training of a GRBFN ultimately settles down to a large-scale non-linear optimisation problem. A novel sequential back-fit algorithm was developed for training the GRBFNs, which enabled the optimisation to proceed one neurone at a time. The new algorithm was tested with very promising results and its application to a simple chemical engineering process was demonstrated In some applications the overall response is composed of sharp localised features superimposed on a gently varying global background. Existing multivariate regression techniques as well as conventional neural networks are aimed at filtering the noise and recovering the overall response. An initial attempt was made at developing an Adaptive GRBFN to separate the local and global features. An efficient algorithm was developed simply by insisting that all the activation functions which are responsible for capturing the global trend should lie in the null space of the differential operator generating the activation function of the kernel based neurones. It was demonstrated that the proposed algorithm performs extremely well in the absence of strong global input interactions.
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Freeman, Jason Alexis Sebastian. "Learning and generalization in radial basis function networks." Thesis, University of Edinburgh, 1998. http://hdl.handle.net/1842/32226.
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The aim of supervised learning is to approximate an unknown target function by adjusting the parameters of a learning model in response to possibly noisy examples generated by the target function. The performance of the learning model at this task can be quantified by examining its generalization ability. Initially the concept of generalization is reviewed, and various methods of measuring it, such as generalization error, prediction error, PAC learning and the evidence, are discussed and the relations between them examined. Some of these relations are dependent on the architecture of the learning model. Two architectures are prevalent in practical supervised learning: the multi-layer perceptron (MLP) and the radial basis function network (RBF). While the RBF has previously been examined from a worst-case perspective, this gives little insight into the performance and phenomena that can be expected in the typical case. This thesis focusses on the properties of learning and generalization that can be expected on average in the RBF. There are two methods in use for training the RBF. The basis functions can be fixed in advance, utilising an unsupervised learning algorithm, or can adapt during the training process. For the case in which the basis functions are fixed, the typical generalization error given a data set of particular size is calculated by employing the Bayesian framework. The effects of noisy data and regularization are examined, the optimal settings of the parameters that control the learning process are calculated, and the consequences of a mismatch between the learning model and the data-generating mechanism are demonstrated. The second case, in which the basis functions are adapted, is studied utilising the on-line learning paradigm. The average evolution of generalization error is calculated in a manner which allows the phenomena of the learning process, such as the specialization of the basis functions, to be elucidated. The three most important stages of training: the symmetric phase, the symmetry-breaking phase and the convergence phase, are analyzed in detail; the convergence phase analysis allows the derivation of maximal and optimal learning rates. Noise on both the inputs and outputs of the data-generating mechanism is introduced, and the consequences examined. Regularization via weight decay is also studied, as are the effects of the learning model being poorly matched to the data generator.
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Langdell, Stephen James. "Radial basis function networks for modelling real world data." Thesis, University of Huddersfield, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.285590.
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Triastuti, Sugiyarto Endang. "Analysing rounding data using radial basis function neural networks model." Thesis, University of Northampton, 2007. http://nectar.northampton.ac.uk/2809/.
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Unspecified counting practices used in a data collection may create rounding to certain ‘based’ number that can have serious consequences on data quality. Statistical methods for analysing missing data are commonly used to deal with the issue but it could actually aggravate the problem. Rounded data are not missing data, instead some observations were just systematically lumped to certain based numbers reflecting the rounding process or counting behaviour. A new method to analyse rounded data would therefore be academically valuable. The neural network model developed in this study fills the gap and serves the purpose by complementing and enhancing the conventional statistical methods. The model detects, analyses, and quantifies the existence of periodic structures in a data set because of rounding. The robustness of the model is examined using simulated data sets containing specific rounding numbers of different levels. The model is also subjected to theoretical and numerical tests to confirm its validity before being used on real applications. Overall, the model performs very well making it suitable for many applications. The assessment results show the importance of using the right best fit in rounding detection. The detection power and cut-off point estimation also depend on data distribution and rounding based numbers. Detecting rounding of prime numbers is easier than non-prime numbers due to the unique characteristics of the former. The bigger the number, the easier is the detection. This is in a complete contrast with non-prime numbers, where the bigger the number, the more will be the “factor” numbers distracting rounding detection. Using uniform best fit on uniform data produces the best result and lowest cut-off point. The consequence of using a wrong best fit on uniform data is however also the worst. The model performs best on data containing 10-40% rounding levels as less or more rounding levels produce unclear rounding pattern or distort the rounding detection, respectively. The modulo-test method also suffers the same problem. Real data applications on religious census data confirms the modulo-test finding that the data contains rounding base 5, while applications on cigarettes smoked and alcohol consumed data show good detection results. The cigarettes data seem to contain rounding base 5, while alcohol consumption data indicate no rounding patterns that may be attributed to the ways the two data were collected. The modelling applications can be extended to other areas in which rounding is common and can have significant consequences. The modelling development can he refined to include data-smoothing process and to make it user friendly as an online modelling tool. This will maximize the model’s potential use
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Mayes, David J. "Implementing radial basis function neural networks in pulsed analogue VLSI." Thesis, University of Edinburgh, 1997. http://hdl.handle.net/1842/15299.
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The Radial Basis Function (RBF) neural network architecture is a powerful computing paradigm that can solve complex classification, recognition and prediction problems. Although the RBF is similar in structure to the ubiquitous Multilayer Perceptron (MLP) neural architecture, it operates in a different way. This thesis discusses the issues addressed, and the findings from, a project that involved implementing a Radial Basis Function neural network in analogue CMOS VLSI. The developed hardware exploits the pulse width modulation (PWM) neural method, which allows compact, low power hardware to be realised through a combination of analogue and digital VLSI techniques. Novel pulsed circuits were designed and developed, fabricated and tested in pursuit of a fully functioning RBF demonstrator chip. The theory underpinning the designs is discussed and measured hardware results from two test chips are presented along with an assessment of circuit performance. Although the circuits generally functioned as required, discrepancies between the actual and theoretical operation were observed. Thus suggested improvements to the original designs are made and the circuit and system level considerations for the final demonstrator chip are discussed. Measured results are presented from the final demonstrator chip, confirming the correct operation of its constituent circuits, along with results from experiments showing that, when modelled in software, the developed circuitry is capable of performing as well as an identically trained RBF with Gaussian non-linearities. However, further results indicated that the expected network performance would degrade when the neural parameters are quantised. Hardware experiments with the demonstrator chip indicated that it could be used as an RBF classifier, but its performance degraded for more complex problems. A summary of the probable reasons for the performance degradation is provided.
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Murphy, Ethan Kane. "Radial-Basis-Function Neural Network Optimization of Microwave Systems." Digital WPI, 2003. https://digitalcommons.wpi.edu/etd-theses/77.
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An original approach in microwave optimization, namely, a neural network procedure combined with the full-wave 3D electromagnetic simulator QuickWave-3D implemented a conformal FDTD method, is presented. The radial-basis-function network is trained by simulated frequency characteristics of S-parameters and geometric data of the corresponding system. High accuracy and computational efficiency of the procedure is illustrated for a waveguide bend, waveguide T-junction with a post, and a slotted waveguide as a radiating element.
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Murphy, Ethan Kane. "Radial-Basis-Function Neural Network Optimization of Microwave Systems." Link to electronic thesis, 2002. http://www.wpi.edu/Pubs/ETD/Available/etd-0113103-121206/.
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Master's Project (M.S.) -- Worcester Polytechnic Institute.Keywords: optimization technique; microwave systems; optimization technique; neural networks; QuickWave 3D. Includes bibliographical references (p. 68-71).
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Middleton, Neil. "Computational analyses of spatial information processing using radial basis function networks." Thesis, Brunel University, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.389984.
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McGarry, Kenneth J. "Rule extraction and knowledge transfer from radial basis function neural networks." Thesis, University of Sunderland, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.391744.
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Sjödin, Hällstrand Andreas. "Bilinear Gaussian Radial Basis Function Networks for classification of repeated measurements." Thesis, Linköpings universitet, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-170850.
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The Growth Curve Model is a bilinear statistical model which can be used to analyse several groups of repeated measurements. Normally the Growth Curve Model is defined in such a way that the permitted sampling frequency of the repeated measurement is limited by the number of observed individuals in the data set.In this thesis, we examine the possibilities of utilizing highly frequently sampled measurements to increase classification accuracy for real world data. That is, we look at the case where the regular Growth Curve Model is not defined due to the relationship between the sampling frequency and the number of observed individuals. When working with this high frequency data, we develop a new method of basis selection for the regression analysis which yields what we call a Bilinear Gaussian Radial Basis Function Network (BGRBFN), which we then compare to more conventional polynomial and trigonometrical functional bases. Finally, we examine if Tikhonov regularization can be used to further increase the classification accuracy in the high frequency data case.Our findings suggest that the BGRBFN performs better than the conventional methods in both classification accuracy and functional approximability. The results also suggest that both high frequency data and furthermore Tikhonov regularization can be used to increase classification accuracy.
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Giani, Alfredo. "Symmetric radial basis function networks and their application to video de-interlacing." Thesis, University of Southampton, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.274076.
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Al-Hindi, Khalid A. "Flexible basis function neural networks for efficient analog implementations /." free to MU campus, to others for purchase, 2002. http://wwwlib.umi.com/cr/mo/fullcit?p3074367.
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Kamat, Sai Shyamsunder. "Analyzing Radial Basis Function Neural Networks for predicting anomalies in Intrusion Detection Systems." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-259187.
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In the 21st century, information is the new currency. With the omnipresence of devices connected to the internet, humanity can instantly avail any information. However, there are certain are cybercrime groups which steal the information. An Intrusion Detection System (IDS) monitors a network for suspicious activities and alerts its owner about an undesired intrusion. These commercial IDS’es react after detecting intrusion attempts. With the cyber attacks becoming increasingly complex, it is expensive to wait for the attacks to happen and respond later. It is crucial for network owners to employ IDS’es that preemptively differentiate a harmless data request from a malicious one. Machine Learning (ML) can solve this problem by recognizing patterns in internet traffic to predict the behaviour of network users. This project studies how effectively Radial Basis Function Neural Network (RBFN) with Deep Learning Architecture can impact intrusion detection. On the basis of the existing framework, it asks how well can an RBFN predict malicious intrusive attempts, especially when compared to contemporary detection practices.Here, an RBFN is a multi-layered neural network model that uses a radial basis function to transform input traffic data. Once transformed, it is possible to separate the various traffic data points using a single straight line in extradimensional space. The outcome of the project indicates that the proposed method is severely affected by limitations. E.g. the model needs to be fine tuned over several trials to achieve a desired accuracy. The results of the implementation show that RBFN is accurate at predicting various cyber attacks such as web attacks, infiltrations, brute force, SSH etc, and normal internet behaviour on an average 80% of the time. Other algorithms in identical testbed are more than 90% accurate. Despite the lower accuracy, RBFN model is more than 94% accurate at recording specific kinds of attacks such as Port Scans and BotNet malware. One possible solution is to restrict this model to predict only malware attacks and use different machine learning algorithm for other attacks.I det 21: a århundradet är information den nya valutan. Med allnärvaro av enheter anslutna till internet har mänskligheten tillgång till information inom ett ögonblick. Det finns dock vissa grupper som använder metoder för att stjäla information för personlig vinst via internet. Ett intrångsdetekteringssystem (IDS) övervakar ett nätverk för misstänkta aktiviteter och varnar dess ägare om ett oönskat intrång skett. Kommersiella IDS reagerar efter detekteringen av ett intrångsförsök. Angreppen blir alltmer komplexa och det kan vara dyrt att vänta på att attackerna ska ske för att reagera senare. Det är avgörande för nätverksägare att använda IDS:er som på ett förebyggande sätt kan skilja på oskadlig dataanvändning från skadlig. Maskininlärning kan lösa detta problem. Den kan analysera all befintliga data om internettrafik, känna igen mönster och förutse användarnas beteende. Detta projekt syftar till att studera hur effektivt Radial Basis Function Neural Networks (RBFN) med Djupinlärnings arkitektur kan påverka intrångsdetektering. Från detta perspektiv ställs frågan hur väl en RBFN kan förutsäga skadliga intrångsförsök, särskilt i jämförelse med befintliga detektionsmetoder.Här är RBFN definierad som en flera-lagers neuralt nätverksmodell som använder en radiell grundfunktion för att omvandla data till linjärt separerbar. Efter en undersökning av modern litteratur och lokalisering av ett namngivet dataset användes kvantitativ forskningsmetodik med prestanda indikatorer för att utvärdera RBFN: s prestanda. En Random Forest Classifier algorithm användes också för jämförelse. Resultaten erhölls efter en serie finjusteringar av parametrar på modellerna. Resultaten visar att RBFN är korrekt när den förutsäger avvikande internetbeteende i genomsnitt 80% av tiden. Andra algoritmer i litteraturen beskrivs som mer än 90% korrekta. Den föreslagna RBFN-modellen är emellertid mycket exakt när man registrerar specifika typer av attacker som Port Scans och BotNet malware. Resultatet av projektet visar att den föreslagna metoden är allvarligt påverkad av begränsningar. T.ex. så behöver modellen finjusteras över flera försök för att uppnå önskad noggrannhet. En möjlig lösning är att begränsa denna modell till att endast förutsäga malware-attacker och använda andra maskininlärnings-algoritmer för andra attacker.
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Choy, Kin-yee, and 蔡建怡. "On modelling using radial basis function networks with structure determined by support vector regression." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2004. http://hub.hku.hk/bib/B29329619.
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Craddock, Rachel Joy. "Multi layered radial basis function networks and the application of state space control theory to feedforward neural networks." Thesis, University of Reading, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.360753.
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Vural, Hulya. "Comparison Of Rough Multi Layer Perceptron And Rough Radial Basis Function Networks Using Fuzzy Attributes." Master's thesis, METU, 2004. http://etd.lib.metu.edu.tr/upload/12605293/index.pdf.
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The hybridization of soft computing methods of Radial Basis Function (RBF) neural networks, Multi Layer Perceptron (MLP) neural networks with back-propagation learning, fuzzy sets and rough sets are studied in the scope of this thesis. Conventional MLP, conventional RBF, fuzzy MLP, fuzzy RBF, rough fuzzy MLP, and rough fuzzy RBF networks are compared. In the fuzzy neural networks implemented in this thesis, the input data and the desired outputs are given fuzzy membership values as the fuzzy properties &ldquolow&rdquo
, &ldquo
medium&rdquo
and &ldquo
high&rdquo
. In the rough fuzzy MLP, initial weights and near optimal number of hidden nodes are estimated using rough dependency rules. A rough fuzzy RBF structure similar to the rough fuzzy MLP is proposed. The rough fuzzy RBF was inspected whether dependencies like the ones in rough fuzzy MLP can be concluded.
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Shankar, Praveen. "Self-organizing radial basis function networks for adaptive flight control and aircraft engine state estimation." Columbus, Ohio : Ohio State University, 2007. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1186767939.
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Charalabopoulos, Grigorios. "Radial basis function neural networks for channel equalization and co-channel interference cancellation in OFDM." Thesis, King's College London (University of London), 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.416116.
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LACERDA, Estefane George Macedo de. "Model Selection of RBF Networks Via Genetic Algorithms." Universidade Federal de Pernambuco, 2003. https://repositorio.ufpe.br/handle/123456789/1845.
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Previous issue date: 2003Um dos principais obstáculos para o uso em larga escala das Redes Neurais é a dificuldade de definir valores para seus parâmetros ajustáveis. Este trabalho discute como as Redes Neurais de Funções Base Radial (ou simplesmente Redes RBF) podem ter seus parâmetros ajustáveis definidos por algoritmos genéticos (AGs). Para atingir este objetivo, primeiramente é apresentado uma visão abrangente dos problemas envolvidos e as diferentes abordagens utilizadas para otimizar geneticamente as Redes RBF. É também proposto um algoritmo genético para Redes RBF com codificação genética não redundante baseada em métodos de clusterização. Em seguida, este trabalho aborda o problema de encontrar os parâmetros ajustáveis de um algoritmo de aprendizagem via AGs. Este problema é também conhecido como o problema de seleção de modelos. Algumas técnicas de seleção de modelos (e.g., validação cruzada e bootstrap) são usadas como funções objetivo do AG. O AG é modificado para adaptar-se a este problema por meio de heurísticas tais como narvalha de Occam e growing entre outras. Algumas modificações exploram características do AG, como por exemplo, a abilidade para resolver problemas de otimização multiobjetiva e manipular funções objetivo com ruído. Experimentos usando um problema benchmark são realizados e os resultados alcançados, usando o AG proposto, são comparados com aqueles alcançados por outras abordagens. As técnicas propostas são genéricas e podem também ser aplicadas a um largo conjunto de algoritmos de aprendizagem
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Yee, Paul V. "Regularized radial basis function networks, theory and applications to probability estimation, classification, and time series prediction." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape11/PQDD_0006/NQ42774.pdf.
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Fung, Chi Fung. "On-line dynamical system modelling using radial basis function networks in adaptive non-linear noise cancellation." Thesis, University of Sheffield, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.389790.
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Tetteh, John. "Enhanced target factor analysis and radial basis function neural networks for analytical spectroscopy and quantitative structure activity relationships." Thesis, University of Greenwich, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.363872.
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Buchan, L. William. "Standard CMOS floating gate memories for non-volatile parameterisation of pulse-stream VLSI radial basis function neural networks." Thesis, University of Edinburgh, 1997. http://hdl.handle.net/1842/13213.
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Analogue VLSI artificial neural networks (ANNs) offer a means of dealing with the non-linearities, cross-sensitivities, noise and interfacing requirements of analogue sensors (the problem of sensor fusion) whilst maintaining the compactness and low power of direct analogue operation. Radial Basis Function (RBF) networks, as a means of performing this function, have several advantages over other ANNs. The pulse-stream ANN technique developed at Edinburgh provides the additional benefit of implicit analogue-digital conversion and signal robustness. However, progressing this work requires the integration of high density analogue memory for parameterisation of the ANN since conventional weight refresh methods are too area and power hungry. For this purpose, standard CMOS floating gates have been proposed as these maintain the low process cost and energy availability of the neural circuitry. Investigation of this proposition proceeded in three stages: 1. Evaluation of the suitability of a standard process for the fabrication of floating gates and exposure of the issues involved: feasibility, analogue programmability, layout optimisation and modelling. 2. The interfacing of floating gates to Radial Basis Function (RBF) neural network circuits and development of programming approaches to cope with potentially destructive characteristics of high voltages and currents. 3. Development of circuits for programming floating gates using continuous-time feedback to facilitate a rapid weight downloading phase from a software model. Three chips were designed, fabricated and tested to explore each of these sets of issues. Detailed discussion and measurements are presented. Conclusions have been drawn about layout optimisation, programmability and device ageing and on the design and general suitability for purpose of standard CMOS floating gates. While these can be designed, interfaced to RBF circuits, and programmed to perform useful functions, their disadvantages make them more useful as a prototyping technique than as memory modules for inclusion in a final product.
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Lu, Weiying. "Development of Radial Basis Function Cascade Correlation Networks and Applications of Chemometric Techniques for Hyphenated Chromatography-Mass Spectrometry Analysis." Ohio University / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1317231072.
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Sahin, Ferat. "A Radial Basis Function Approach to a Color Image Classification Problem in a Real Time Industrial Application." Thesis, Virginia Tech, 1997. http://hdl.handle.net/10919/36847.
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In this thesis, we introduce a radial basis function network approach to solve a color image classification problem in a real time industrial application. Radial basis function networks are employed to classify the images of finished wooden parts in terms of their color and species. Other classification methods are also examined in this work. The minimum distance classifiers are presented since they have been employed by the previous research.
We give brief definitions about color space, color texture, color quantization, color classification methods. We also give an intensive review of radial basis functions, regularization theory, regularized radial basis function networks, and generalized radial basis function networks. The centers of the radial basis functions are calculated by the k-means clustering algorithm. We examine the k-means algorithm in terms of starting criteria, the movement rule, and the updating rule. The dilations of the radial basis functions are calculated using a statistical method.
Learning classifier systems are also employed to solve the same classification problem. Learning classifier systems learn the training samples completely whereas they are not successful to classify the test samples. Finally, we present some simulation results for both radial basis function network method and learning classifier systems method. A comparison is given between the results of each method. The results show that the best classification method examined in this work is the radial basis function network method.Master of Science
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Lee, Hee Eun. "Hierarchical modeling of multi-scale dynamical systems using adaptive radial basis function neural networks: application to synthetic jet actuator wing." Thesis, Texas A&M University, 2003. http://hdl.handle.net/1969.1/230.
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To obtain a suitable mathematical model of the input-output behavior of highly nonlinear, multi-scale, nonparametric phenomena, we introduce an adaptive radial basis function approximation approach. We use this approach to estimate the discrepancy between traditional model areas and the multiscale physics of systems involving distributed sensing and technology. Radial Basis Function Networks offers the possible approach to nonparametric multi-scale modeling for dynamical systems like the adaptive wing with the Synthetic Jet Actuator (SJA). We use the Regularized Orthogonal Least Square method (Mark, 1996) and the RAN-EKF (Resource Allocating Network-Extended Kalman Filter) as a reference approach. The first part of the algorithm determines the location of centers one by one until the error goal is met and regularization is achieved. The second process includes an algorithm for the adaptation of all the parameters in the Radial Basis Function Network, centers, variances (shapes) and weights. To demonstrate the effectiveness of these algorithms, SJA wind tunnel data are modeled using this approach. Good performance is obtained compared with conventional neural networks like the multi layer neural network and least square algorithm. Following this work, we establish Model Reference Adaptive Control (MRAC) formulations using an off-line Radial Basis Function Networks (RBFN). We introduce the adaptive control law using a RBFN. A theory that combines RBFN and adaptive control is demonstrated through the simple numerical simulation of the SJA wing. It is expected that these studies will provide a basis for achieving an intelligent control structure for future active wing aircraft.
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Altran, Alessandra Bonato [UNESP]. "Sistema inteligente para previsão de carga multinodal em sistemas elétricos de potência." Universidade Estadual Paulista (UNESP), 2010. http://hdl.handle.net/11449/100304.
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A previsão de carga, em sistemas de energia elétrica, constitui-se numa atividade de grande importância, tendo em vista que a maioria dos estudos realizados (fluxo de potência, despacho econômico, planejamento da expansão, compra e venda de energia, etc.) somente poderá ser efetivada se houver a disponibilidade de uma boa estimativa da carga a ser atendida. Deste modo, visando contribuir para que o planejamento e operação dos sistemas de energia elétrica ocorram de forma segura, confiável e econômica, foi desenvolvida uma metodologia para previsão de carga, a previsão multinodal, que pode ser entendida como um sistema inteligente que considera vários pontos da rede elétrica durante a realização da previsão. O sistema desenvolvido conta com o uso de uma rede neural artificial composta por vários módulos, sendo esta do tipo perceptron multicamadas, cujo treinamento é baseado no algoritmo retropropagação. Porém, foi realizada uma modificação na função de ativação da rede, em substituição à função usual, a função sigmoide, foram utilizadas as funções de base radial. Tal metodologia foi aplicada ao problema de previsão de cargas elétricas a curto-prazo (24 horas à frente)
Load forecasting in electric power systems is a very important activity due to several studies, e.g. power flow, economic dispatch, expansion planning, purchase and sale of energy that are extremely dependent on a good estimate of the load. Thus, contributing to a safe, reliable, economic and secure operation and planning this work is developed, which is an intelligent system for multinodal electric load forecasting considering several points of the network. The multinodal system is based on an artificial neural network composed of several modules. The neural network is a multilayer perceptron trained by backpropagation where the traditional sigmoide is substituted by radial basis functions. The methodology is applied to forecast loads 24 hours in advance
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Altran, Alessandra Bonato. "Sistema inteligente para previsão de carga multinodal em sistemas elétricos de potência /." Ilha Solteira : [s.n.], 2010. http://hdl.handle.net/11449/100304.
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Resumo: A previsão de carga, em sistemas de energia elétrica, constitui-se numa atividade de grande importância, tendo em vista que a maioria dos estudos realizados (fluxo de potência, despacho econômico, planejamento da expansão, compra e venda de energia, etc.) somente poderá ser efetivada se houver a disponibilidade de uma boa estimativa da carga a ser atendida. Deste modo, visando contribuir para que o planejamento e operação dos sistemas de energia elétrica ocorram de forma segura, confiável e econômica, foi desenvolvida uma metodologia para previsão de carga, a previsão multinodal, que pode ser entendida como um sistema inteligente que considera vários pontos da rede elétrica durante a realização da previsão. O sistema desenvolvido conta com o uso de uma rede neural artificial composta por vários módulos, sendo esta do tipo perceptron multicamadas, cujo treinamento é baseado no algoritmo retropropagação. Porém, foi realizada uma modificação na função de ativação da rede, em substituição à função usual, a função sigmoide, foram utilizadas as funções de base radial. Tal metodologia foi aplicada ao problema de previsão de cargas elétricas a curto-prazo (24 horas à frente)Abstract: Load forecasting in electric power systems is a very important activity due to several studies, e.g. power flow, economic dispatch, expansion planning, purchase and sale of energy that are extremely dependent on a good estimate of the load. Thus, contributing to a safe, reliable, economic and secure operation and planning this work is developed, which is an intelligent system for multinodal electric load forecasting considering several points of the network. The multinodal system is based on an artificial neural network composed of several modules. The neural network is a multilayer perceptron trained by backpropagation where the traditional sigmoide is substituted by radial basis functions. The methodology is applied to forecast loads 24 hours in advance
Orientador: Carlos Roberto. Minussi
Coorientador: Francisco Villarreal Alvarado
Banca: Anna Diva Plasencia Lotufo
Banca: Maria do Carmo Gomes da Silveira
Banca: Gelson da Cruz Junior
Banca: Edmárcio Antonio Belati
Doutor
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Sajjad, Pasha Mohammad. "Machine vision for automating visual inspectionof wooden railway sleepers." Thesis, Högskolan Dalarna, Datateknik, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:du-3120.
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Andrade, Gustavo Araújo de. "PROGRAMAÇÃO DINÂMICA HEURÍSTICA DUAL E REDES DE FUNÇÕES DE BASE RADIAL PARA SOLUÇÃO DA EQUAÇÃO DE HAMILTON-JACOBI-BELLMAN EM PROBLEMAS DE CONTROLE ÓTIMO." Universidade Federal do Maranhão, 2014. http://tedebc.ufma.br:8080/jspui/handle/tede/517.
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Previous issue date: 2014-04-28In this work the main objective is to present the development of learning algorithms for online application for the solution of algebraic Hamilton-Jacobi-Bellman equation. The concepts covered are focused on developing the methodology for control systems, through techniques that aims to design online adaptive controllers to reject noise sensors, parametric variations and modeling errors. Concepts of neurodynamic programming and reinforcement learning are are discussed to design algorithms where the context of a given operating point causes the control system to adapt and thus present the performance according to specifications design. Are designed methods for online estimation of adaptive critic focusing efforts on techniques for gradient estimating of the environment value function.
Neste trabalho o principal objetivo é apresentar o desenvolvimento de algoritmos de aprendizagem para execução online para a solução da equação algébrica de Hamilton-Jacobi-Bellman. Os conceitos abordados se concentram no desenvolvimento da metodologia para sistemas de controle, por meio de técnicas que tem como objetivo o projeto online de controladores adaptativos são projetados para rejeitar ruídos de sensores, variações paramétricas e erros de modelagem. Conceitos de programação neurodinâmica e aprendizagem por reforço são abordados para desenvolver algoritmos onde a contextualização de determinado ponto de operação faz com que o sistema de controle se adapte e, dessa forma, apresente o desempenho de acordo com as especificações de projeto. Desenvolve-se métodos para a estimação online do crítico adaptativo concentrando os esforços em técnicas de estimação do gradiente da função valor do ambiente.
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Tinós, Renato. "Detecção e diagnóstico de falhas em robôs manipuladores via redes neurais artificiais." Universidade de São Paulo, 1999. http://www.teses.usp.br/teses/disponiveis/18/18133/tde-04022002-162950/.
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Neste trabalho, um novo enfoque para detecção e diagnóstico de falhas (DDF) em robôs manipuladores é apresentado. Um robô com falhas pode causar sérios danos e pode colocar em risco o pessoal presente no ambiente de trabalho. Geralmente, os pesquisadores têm proposto esquemas de DDF baseados no modelo matemático do sistema. Contudo, erros de modelagem podem ocultar os efeitos das falhas e podem ser uma fonte de alarmes falsos. Aqui, duas redes neurais artificiais são utilizadas em um sistema de DDF para robôs manipuladores. Um perceptron multicamadas treinado por retropropagação do erro é usado para reproduzir o comportamento dinâmico do manipulador. As saídas do perceptron são comparadas com as variáveis medidas, gerando o vetor de resíduos. Em seguida, uma rede com função de base radial é usada para classificar os resíduos, gerando a isolação das falhas. Quatro algoritmos diferentes são empregados para treinar esta rede. O primeiro utiliza regularização para reduzir a flexibilidade do modelo. O segundo emprega regularização também, mas ao invés de um único termo de penalidade, cada unidade radial tem um regularização individual. O terceiro algoritmo emprega seleção de subconjuntos para selecionar as unidades radiais a partir dos padrões de treinamento. O quarto emprega o mapa auto-organizável de Kohonen para fixar os centros das unidades radiais próximos aos centros dos aglomerados de padrões. Simulações usando um manipulador com dois graus de liberdade e um Puma 560 são apresentadas, demostrando que o sistema consegue detectar e diagnosticar corretamente falhas que ocorrem em conjuntos de padrões não-treinados.In this work, a new approach for fault detection and diagnosis in robotic manipulators is presented. A faulty robot could cause serious damages and put in risk the people involved. Usually, researchers have proposed fault detection and diagnosis schemes based on the mathematical model of the system. However, modeling errors could obscure the fault effects and could be a false alarm source. In this work, two artificial neural networks are employed in a fault detection and diagnosis system to robotic manipulators. A multilayer perceptron trained with backpropagation algorithm is employed to reproduce the robotic manipulator dynamical behavior. The perceptron outputs are compared with the real measurements, generating the residual vector. A radial basis function network is utilized to classify the residual vector, generating the fault isolation. Four different algorithms have been employed to train this network. The first utilizes regularization to reduce the flexibility of the model. The second employs regularization too, but instead of only one penalty term, each radial unit has a individual penalty term. The third employs subset selection to choose the radial units from the training patterns. The forth algorithm employs the Kohonens self-organizing map to fix the radial unit center near to the cluster centers. Simulations employing a two link manipulator and a Puma 560 manipulator are presented, demonstrating that the system can detect and isolate correctly faults that occur in nontrained pattern sets.
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Bandreddy, Neel Kamal. "Estimation of Unmeasured Radon Concentrations in Ohio Using Quantile Regression Forest." University of Toledo / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1418311498.
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Ghosh, Dastidar Samanwoy. "Models of EEG data mining and classification in temporal lobe epilepsy: wavelet-chaos-neural network methodology and spiking neural networks." Columbus, Ohio : Ohio State University, 2007. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1180459585.
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Pohlídal, Antonín. "Inteligentní emailová schránka." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2012. http://www.nusl.cz/ntk/nusl-236458.
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This master's thesis deals with the use of text classification for sorting of incoming emails. First, there is described the Knowledge Discovery in Databases and there is also analyzed in detail the text classification with selected methods. Further, this thesis describes the email communication and SMTP, POP3 and IMAP protocols. The next part contains design of the system that classifies incoming emails and there are also described realated technologie ie Apache James Server, PostgreSQL and RapidMiner. Further, there is described the implementation of all necessary components. The last part contains an experiments with email server using Enron Dataset.
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Lamraoui, Mourad. "Surveillance des centres d'usinage grande vitesse par approche cyclostationnaire et vitesse instantanée." Phd thesis, Université Jean Monnet - Saint-Etienne, 2013. http://tel.archives-ouvertes.fr/tel-01001576.
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Dans l'industrie de fabrication mécanique et notamment pour l'utilisation des centres d'usinage haute vitesse, la connaissance des propriétés dynamiques du système broche-outil-pièce en opération est d'une grande importance. L'accroissement des performances des machines-outils et des outils de coupe a œuvré au développement de ce procédé compétitif. D'innombrables travaux ont été menés pour accroître les performances et les remarquables avancées dans les matériaux, les revêtements des outils coupants et les lubrifiants ont permis d'accroître considérablement les vitesses de coupe tout en améliorant la qualité de la surface usinée. Cependant, l'utilisation rationnelle de cette technologie est encore fortement pénalisée par les lacunes dans la connaissance de la coupe, que ce soit au niveau microscopique des interactions fines entre l'outil et la matière coupée, aussi bien qu'au niveau macroscopique intégrant le comportement de la cellule élémentaire d'usinage, si bien que le comportement dynamique en coupe garde encore une grande part de questionnement et exige de l'utilisateur un bon niveau de savoir-faire et parfois d'empirisme pour exploiter au mieux les capacités des moyens de production. Le fonctionnement des machines d'usinage engendre des vibrations qui sont souvent la cause des dysfonctionnements et accélère l'usure des composantes mécaniques (roulements) et outils. Ces vibrations sont une image des efforts internes des systèmes, d'où l'intérêt d'analyser les grandeurs mécaniques vibratoires telle que la vitesse ou l'accélération vibratoire. Ces outils sont indispensables pour une maintenance moderne dont l'objectif est de réduire les coûts liés aux pannes
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Luka, Mejić. "Методе аутоматске конфигурације софт сензора." Phd thesis, Univerzitet u Novom Sadu, Fakultet tehničkih nauka u Novom Sadu, 2019. https://www.cris.uns.ac.rs/record.jsf?recordId=110926&source=NDLTD&language=en.
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Математички модели за естимацију тешко мерљивих величина називајусе софт сензорима. Процес формирања софт сензора није тривијалан иквалитет естимације тешко мерљиве величине директно зависи одначина формирања. Недостаци постојећих алгоритама за формирањеспречавају аутоматску конфигурацију софт сензора. У овом раду суреализовани нови алгоритми који имају за сврху аутоматизацијуконфигурације софт сензора. Реализовани алгоритми решавајупроблеме проналаска оптималног сета улаза у софт сензор и кашњењасваког од њих као и одабира структуре и начина обуке софт сензоразаснованих на вештачким неуронским мрежама са радијално базиранимфункцијама.Matematički modeli za estimaciju teško merljivih veličina nazivajuse soft senzorima. Proces formiranja soft senzora nije trivijalan ikvalitet estimacije teško merljive veličine direktno zavisi odnačina formiranja. Nedostaci postojećih algoritama za formiranjesprečavaju automatsku konfiguraciju soft senzora. U ovom radu surealizovani novi algoritmi koji imaju za svrhu automatizacijukonfiguracije soft senzora. Realizovani algoritmi rešavajuprobleme pronalaska optimalnog seta ulaza u soft senzor i kašnjenjasvakog od njih kao i odabira strukture i načina obuke soft senzorazasnovanih na veštačkim neuronskim mrežama sa radijalno baziranimfunkcijama.
Mathematical models that are used for estimation of variables that can not bemeasured in real time are called soft sensors. Creation of soft sensor is acomplex process and quality of estimation depends on the way soft sensor iscreated. Restricted applicability of existing algorithms is preventing automaticconfiguration of soft sensors. This paper presents new algorithms that areproviding automatic configuration of soft sensors. Presented algorithms arecapable of determing optimal subset of soft sensor inputs and their timedelays, as well as optimal architecture and automatic training of the softsensors that are based on artificial radial basis function networks.
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Vijaya, Kumar M. "System Identification And Control Of Helicopter Using Neural Networks." Thesis, 2010. http://etd.iisc.ernet.in/handle/2005/1977.
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The present work focuses on the two areas of investigation: system identification of helicopter and design of controller for the helicopter.
Helicopter system identification, the first subject of investigation in this thesis, can be described as the extraction of system characteristics/dynamics from measured flight test data. Wind tunnel experimental data suffers from scale effects and model deficiencies. The increasing need for accurate models for the design of high bandwidth control system for helicopters has initiated a renewed interest in and a more active use of system identification. Besides, system identification is likely to become mandatory in the future for model validation of ground based helicopter simulators. Such simulators require accurate models in order to be accepted by pilots and regulatory authorities like Federal Aviation Regulation for realistic complementary helicopter mission training.
Two approaches are widely used for system identification, namely, black box and gray box approach. In the black-box approach, the relationship between input-output data is approximated using nonparametric methods such as neural networks and in such a case, internal details of the system and model structure may not be known. In the gray box approach, parameters are estimated after defining the model structure. In this thesis, both black box and gray box approaches are investigated.
In the black box approach, in this thesis, a comparative study and analysis of different Recurrent Neural Networks(RNN) for the identification of helicopter dynamics using flight data is investigated. Three different RNN architectures namely, Nonlinear Auto Regressive eXogenous input(NARX) model, neural network with internal memory known as Memory Neuron Networks(MNN)and Recurrent MultiLayer perceptron (RMLP) networks are used to identify dynamics of the helicopter at various flight conditions. Based on the results, the practical utility, advantages and limitations of the three models are critically appraised and it is found that the NARX model is most suitable for the identification of helicopter dynamics.
In the gray box approach, helicopter model parameters are estimated after defining the model structure. The identification process becomes more difficult as the number of degrees-of-freedom and model parameters increase. To avoid the drawbacks of conventional methods, neural network based techniques, called the delta method is investigated in this thesis. This method does not require initial estimates of the parameters and the parameters can be directly extracted from the flight data. The Radial Basis Function Network(RBFN)is used for the purpose of estimation of parameters. It is shown that RBFN is able to satisfactorily estimate stability and control derivatives using the delta method.
The second area of investigation addressed in this thesis is the control of helicopter in flight. Helicopter requires use of a control system to achieve satisfactory flight. Designing a classical controller involves developing a nonlinear model of the helicopter and extracting linearized state space matrices from the nonlinear model at various flight conditions. After examining the stability characteristics of the helicopter, the desired response is obtained using a feedback control system. The scheduling of controller gains over the entire envelope is used to obtain the desired response.
In the present work, a helicopter having a soft inplane four bladed hingeless main rotor and a four-bladed tail rotor with conventional mechanical controls is considered. For this helicopter, a mathematical model and also a model based on neural network (using flight data) has been developed.
As a precursor, a feed back controller, the Stability Augmentation System(SAS), is designed using linear quadratic regulator control with full state feedback and LQR with out put feedback approaches. SAS is designed to meet the handling qualities specification known as Aeronautical Design Standard ADS-33E-PRF. The control gains have been tuned with respect to forward speed and gain scheduling has been arrived at. The SAS in the longitudinal axis meets the requirement of the Level1 handling quality specifications in hover and low speed as well as for forward speed flight conditions. The SAS in the lateral axis meets the requirement of the Level2 handling quality specifications in both hover and low speed as well as for forward speed flight conditions.
Such conventional design of control has served useful purposes, however, it requires considerable flight testing which is time consuming, to demonstrate and tune these control law gains. In modern helicopters, the stringent requirements and non-linear maneuvers make the controller design further complicated. Hence, new design tools have to be explored to control such helicopters. Among the many approaches in adaptive control, neural networks present a potential alternative for modeling and control of nonlinear dynamical systems due to their approximating capabilities and inherent adaptive features. Furthermore, from a practical perspective, the massive parallelism and fast adaptability of neural network implementations provide more incentive for further investigation in problems involving dynamical systems with unknown non-linearity. Therefore, adaptive control approach based on neural networks is proposed in this thesis.
A neural network based Feedback Error Neural adaptive Controller(FENC) is designed for a helicopter. The proposed controller scheme is based on feedback error learning strategy in which the outer loop neural controller enhances the inner loop conventional controller by compensating for unknown non-linearity and parameter un-certainties. Nonlinear Auto Regressive eXogenous input(NARX)neural network architecture is used to approximate the control law and the controller network parameters are adapted using updated rules Lyapunov synthesis. An offline (finite time interval)and on-line adaptation strategy is used to approximate system uncertainties. The results are validated using simulation studies on helicopter undergoing an agile maneuver. The study shows that the neuro-controller meets the requirements of ADS-33 handling quality specifications.
Even though the tracking error is less in FENC scheme, the control effort required to follow the command is very high. To overcome these problems, a Direct Adaptive Neural Control(DANC)scheme to track the rate command signal is presented. The neural controller is designed to track rate command signal generated using the reference model. For the simulation study, a linearized helicopter model at different straight and level flight conditions is considered. A neural network with a linear filter architecture trained using back propagation through time is used to approximate the control law. The controller network parameters are adapted using updated rules Lyapunov synthesis. The off-line trained (for finite time interval)network provides the necessary stability and tracking performance. The on-line learning is used to adapt the network under varying flight conditions. The on-line learning ability is demonstrated through parameter uncertainties. The performance of the proposed direct adaptive neural controller is compared with feedback error learning neural controller. The performance of the controller has been validated at various flight conditions. The theoretical results are validated using simulation studies based on a nonlinear six degree-of-freedom helicopter undergoing an agile maneuver. Realistic gust and sensor noise are added to the system to study the disturbance rejection properties of the neural controllers. To investigate the on-line learning ability of the proposed neural controller, different fault scenarios representing large model error and control surface loss are considered. The performances of the proposed DANC scheme is compared with the FENC scheme. The study shows that the neuro-controller meets the requirements of ADS-33 handling quality specifications.
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Kumar, Rajan. "A Neural Network Approach To Rotorcraft Parameter Estimation." Thesis, 2007. http://hdl.handle.net/2005/549.
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The present work focuses on the system identification method of aerodynamic parameter estimation which is used to calculate the stability and control derivatives required for aircraft flight mechanics. A new rotorcraft parameter estimation technique is proposed which uses a type of artificial neural network (ANN) called radial basis function network (RBFN). Rotorcraft parameter estimation using ANN is an unexplored research topic and the earlier works in this area have used the output error, equation error and filter error methods which are conventional parameter estimation methods. However, the conventional methods require an accurate non-linear rotorcraft simulation model which is not required by the ANN based method. The application of RBFN overcomes the drawbacks of multilayer perceptron (MLP) based delta method of parameter estimation and gives satisfactory results at either end of the ordered set of estimates. This makes the RBFN based delta method for parameter estimation suitable for rotorcraft studies, as both transition and high speed flight regime characteristics can be studied. The RBFN based delta method for parameter estimation is used for computation of aerodynamic parameters from both simulated and real time flight data. The simulated data is generated from an 8-DoF non-linear simulation model based on the Level-1 criteria of rotorcraft simulation modeling. The generated simulated data is used for computation of the quasi-steady and the time-variant stability and control parameters for different flight conditions using the RBFN based delta method. The performance of RBFN based delta method is also analyzed in the presence of state and measurement noise as well as outliers. The established methodology is then applied to compute parameters directly from real time flight test data for a BO 105 S123 helicopter obtained from DLR (German Aerospace Center). The parameters identified using the RBFN based delta method are compared with the identified values for the BO 105 helicopter from published literature which have used conventional parameter estimation techniques for parameter estimation using a 6-DoF and a 9-DoF rotorcraft simulation model. Finally, the estimated parameters are verified from the flight data generated by a frequency sweep pilot control input for assessing the predictive capability of the RBFN based delta method. Since the approach directly computes the parameters from flight data, it can be used for a reliable description of the higher frequency range, which is needed for high bandwidth flight control and in-flight simulation.
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Tsai, Yen-lung, and 蔡炎龍. "Dynamical Radial Basis Function Networks and Chaotic Forecasting." Thesis, 1993. http://ndltd.ncl.edu.tw/handle/02000011855628912218.
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碩士國立政治大學
應用數學研究所
81
The forecasting technique is important for many researches and applications. In this paper, we shall construct a new model of neural networks -- the dynamical radial basis function (DRBF) networks and use the DRBF networks as "function approximators" to solve some forecasting problems. Different learning algorithms are used to test the capability of DRBF networks.
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Liao, Shih-hui, and 廖時慧. "Study on Additive Generalized Radial Basis Function Networks." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/n89ckq.
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碩士國立中山大學
電機工程學系研究所
97
In this thesis, we propose a new class of learning models, namely the additive generalized radial basis function networks (AGRBFNs), for general nonlinear regression problems. This class of learning machines combines the generalized radial basis function networks (GRBFNs) commonly used in general machine learning problems and the additive models (AMs) frequently encountered in semiparametric regression problems. In statistical regression theory, AM is a good compromise between the linear model and the nonparametric model. In order for more general network structure hoping to address more general data sets, the AMs are embedded in the output layer of the GRBFNs to form the AGRBFNs. Simple weights updating rules based on incremental gradient descent will be derived. Several illustrative examples are provided to compare the performances for the classical GRBFNs and the proposed AGRBFNs. Simulation results show that upon proper selection of the hidden nodes and the bandwidth of the kernel smoother used in additive output layer, AGRBFNs can give better fits than the classical GRBFNs. Furthermore, for the given learning problem, AGRBFNs usually need fewer hidden nodes than those of GRBFNs for the same level of accuracy.
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Jeng, Chen Shuenn, and 陳舜政. "Hybrid Learning Alogrithm for Radial Basis Function Networks." Thesis, 1993. http://ndltd.ncl.edu.tw/handle/35796206072974480739.
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碩士國立臺灣大學
化學工程研究所
81
This thesis studies the fundamental properties of Radial Basis Functions and the Radial Basis Function Networks. The application of RBFNs in both static function approximation and process model indentification is also discussed. A rigid definition for a set of RBFs and the basic structure of RBFNs are given at first. Then an effective training algorithm, the hybrid learning algorithm, is presented for the parametric estimation of the network. By including the target values in the input layer of a RBFN, one can obtain more reasonable distribution of centers in hidden nodes layer of the network. The width of each Radial Basis Function Unit is deter- imined by using the average angle from the corresponding center location to its nearest neighbors. The least square algorithm is used for determining values of linear connective weights by minimizing the discrepancy between outputs of the network to its targets. If resulting network obtained from the previous steps could not providing network outputs with required acurracy, optimization methods such as steepest descent can be furtherly used to give network parameters with required accurancy. Owing to on-line application of the network, a recursive method is also proposed for updating parameters of network when given additional training pattern. The results of simulated examples show that various RBFNs are good approximators for static functions as well as dynamic processes. Hence, the RBFNs seem to be a potential black-box model structure for identification of nonlinear chemical processes.
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Mahale, Gopinath Vasanth. "Algorithm And Architecture Design for Real-time Face Recognition." Thesis, 2016. http://etd.iisc.ernet.in/handle/2005/2743.
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Face recognition is a field of biometrics that deals with identification of subjects based on features present in the images of their faces. The factors that make face recognition popular and favorite as compared to other biometric methods are easier operation and ability to identify subjects without their knowledge. With these features, face recognition has become an integral part of the present day security systems, targeting a smart and secure world.
There are various factors that de ne the performance of a face recognition system. The most important among them are recognition accuracy of algorithm used and time taken for recognition. Recognition accuracy of the face recognition algorithm gets affected by changes in pose, facial expression and illumination along with occlusions in the images. There have been a number of algorithms proposed to enable recognition under these ambient changes. However, it has been hard to and a single algorithm that can efficiently recognize faces in all the above mentioned conditions. Moreover, achieving real time performance for most of the complex face recognition algorithms on embedded platforms has been a challenge. Real-time performance is highly preferred in critical applications such as identification of crime suspects in public. As available software solutions for FR have significantly large latency in recognizing individuals, they are not suitable for such critical real-time applications. This thesis focuses on real-time aspect of FR, where acceleration of the algorithms is achieved by means of parallel hardware architectures.
The major contributions of this work are as follows. We target to design a face recognition system that can identify at most 30 faces in each frame of video at 15 frames per second, which amounts to 450 recognitions per second. In addition, we target to achieve good recognition accuracy along with scalability in terms of database size and input image resolutions. To design a system with these specifications, as a first step, we explore algorithms in literature and come up with a hybrid face recognition algorithm. This hybrid algorithm shows good recognition accuracy on face images with changes in illumination, pose and expressions, and also with occlusions. In addition the computations in the algorithm are modular in nature which are suitable for real-time realizations through parallel processing.
The face recognition system consists of a face detection module to detect faces in the input image, which is followed by a face recognition module to identify the detected faces. There are well established algorithms and architectures for face detection in literature which can perform detection at 15 frames per second on video frames. Detected faces of different sizes need to be scaled to the size specified by the face recognition module. To meet the real-time constraints, we propose a hardware architecture for real-time bi-cubic convolution interpolation with dynamic scaling factors. To recognize the resized faces in real-time, a scalable parallel pipelined architecture is designed for the hybrid algorithm which can perform 450 recognitions per second on a database containing grayscale images of at most 450 classes on Virtex 6 FPGA. To provide flexibility and programmability, we extend this design to REDEFINE, a multi-core massively parallel reconfigurable architecture. In this design, we come up with FR specific programmable cores termed Scalable Unit for Region Evaluation (SURE) capable of performing modular computations in the hybrid face recognition algorithm. We replicate SUREs in each tile of REDEFINE to construct a face recognition module termed REDEFINE for Face Recognition using SURE Homogeneous Cores (REFRESH).
There is a need to learn new unseen faces on-line in practical face recognition systems. Considering this, for real-time on-line learning of unseen face images, we design tiny processors termed VOP, Processor for Vector Operations. VOPs function as coprocessors to process elements under each tile of REDEFINE to accelerate micro vector operations appearing in the synaptic weight computations. We also explore deep neural networks which operate similar to the processing in human brain and capable of working on very large face databases. We explore the field of Random matrix theory to come up with a solution for synaptic weight initialization in deep neural networks for better classification . In addition, we perform design space exploration of hardware architecture for deep convolution networks and conclude with directions for future work.
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Yu-Yen, Ou. "A Study on Machine Learning with Radial Basis Function Networks." 2005. http://www.cetd.com.tw/ec/thesisdetail.aspx?etdun=U0001-1107200519171000.
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Huang, Jun-zhi, and 黃俊智. "Iterative Radial Basis Function Networks Channel Estimators for OFDM Systems." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/80301801655832586829.
Full textAbstract:
碩士雲林科技大學
電子與資訊工程研究所
96
Radio signal will be distorted by channel noise and multi-path propagation phenomena during transmission. Estimation of channel response that provides a powerful mean to possibly equalize the distorted signals is a significant technology in modern communication systems. In this thesis, the pilot-aided OFDM system that makes use of some subcarrier to transmit the pilot symbols is considered. Channel responses in data subcarriers can be estimated from the responses of the pilot subcarriers by taking advantages of interpolator methods such as linear interpolator, WMSA, decision-directed channel predictor, robust channel interpolator and RBFN, and compares their performances.
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Ou, Yu-Yen, and 歐昱言. "A Study on Machine Learning with Radial Basis Function Networks." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/41151026801159328196.
Full textAbstract:
博士國立臺灣大學
資訊工程學研究所
93
This thesis reports a series of studies on machine learning with the radial basis function network (RBFN). The first part of this thesis discusses how to construct an RBFN efficiently with the regularization procedure. In fact, construction of an RBFN with the regularization procedure involves two main issues. The first issue concerns the number of hidden nodes to be incorporated and where the centers of the associated kernel functions should be located. The second issue concerns how the links between the hidden layer and the output layer should be weighted. For the first issue, this thesis discusses the effects with a random samples based approach and an incremental clustering based approach. For the second issue, this thesis elaborates the effects with the Cholesky decomposition employed. Experimental results show that an RBFN constructed with the approaches proposed in this thesis is able to deliver the same level of classification accuracy as the SVM and offers several important advantages. Finally, this thesis reports the experimental results with the QuickRBF package, which has been developed based on the approaches proposed in this thesis, applied to bioinformatics problems. The second study reported in this thesis concerns how the novel relaxed variable kernel density estimation (RVKDE) algorithm that our research team has recently proposed performs in data classification applications. The experimental results reveal that the classifier configured with the RVKDE algorithm is capable of delivering the same level of accuracy as the SVM, while enjoying some advantages in comparison with the SVM. In particular, the time complexity for construction of a classifier with the RVKDE algorithm is O(nlogn), where n is the number of samples in the training data set. This means that it is highly efficient to construct a classifier with the RVKDE algorithm, in comparison with the SVM algorithm. Furthermore, the RVKDE based classifier is able to carry out data classification with more than two classes of samples in one single run. In other words, it does not need to invoke mechanisms such as one-against-one or one-against-all for handling data sets with more than two classes of samples. The successful experiences with the RVKDE algorithm in data classification applications then motivate the study presented next in this thesis. In Section 4.3, a RVKDE based data reduction approach for expediting the model selection process of the SVM is described. Experimental results show that, in comparison with the existing approaches, the data reduction based approach proposed in this thesis is able to expedite the model selection process by a larger degree and cause a smaller degradation of prediction accuracy.
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Wu, Mao-Cheng, and 吳茂正. "Evolutionary Radial Basis Function Networks for Nonlinear Time Series Prediction." Thesis, 1997. http://ndltd.ncl.edu.tw/handle/95550039979484340758.
Full textAbstract:
碩士國立臺灣大學
資訊工程學系研究所
85
We propose a new technique, referred as Evolutionary Radial Basis Function Networks (ERBFN), for training Radial Basis Function networks as time series predictors in this thesis. Our method extracts the ideas from both stochastic and deterministic algorithms to evolve both the architectures and center loca- tions and to train the connection weights of Radial Basis Functionnetworks. This algorithm is applied to solve some benchmark nonlinear timeseries predic- tion problems. Comparisons between our method and other algorithmsfor training RBF net works, including K-MEANS clustering algorithm and GeneticAlgorithms, are made to prove its powerful effects in prediction tasks. Asseen in experi- mental results, it also outperforms other techniques based ondifferent network schemes, such as typical neural networks and Genetic Programming.