Relevant bibliographies by topics / Radial basis function networks (RBFNs)

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Radial basis function networks (RBFNs)'

Author: Grafiati
Published: 4 June 2021
Last updated: 4 February 2022

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Journal articles on the topic "Radial basis function networks (RBFNs)"

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Feng, Hsuan Ming, Ching Chang Wong, and Ji Hwei Horng. "RBFNs Nonlinear Control System Design through BFPSO Algorithm." Applied Mechanics and Materials 764-765 (May 2015): 619–23. http://dx.doi.org/10.4028/www.scientific.net/amm.764-765.619.

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All parameters are automatically extracted by the bacterial foraging particle swarm optimization (BFPSO) algorithm to approach the desired control system. Three parameterize basis function neural networks (RBFNs) model to solve the car-pole system problem. Several free parameters of radial basis functions can be automatically tuned by the direct of the specified fitness function. In additional, the proper number of radial basis functions (RBFs) of the constructed RBFNs can be chosen by the defined fitness function which takes this factor into account. The desired multiple objectives of the RBFNs control system are proposed to simultaneously approach the smaller errors with a fewer RBFs number. Simulations show that the developed RBFNs control systems efficiently achieve the desired the setting lot as soon as possible.
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Alqezweeni, Mohie Mortadha, Vladimir Ivanovich Gorbachenko, Maxim Valerievich Zhukov, and Mustafa Sadeq Jaafar. "Efficient Solving of Boundary Value Problems Using Radial Basis Function Networks Learned by Trust Region Method." International Journal of Mathematics and Mathematical Sciences 2018 (June 3, 2018): 1–4. http://dx.doi.org/10.1155/2018/9457578.

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A method using radial basis function networks (RBFNs) to solve boundary value problems of mathematical physics is presented in this paper. The main advantages of mesh-free methods based on RBFN are explained here. To learn RBFNs, the Trust Region Method (TRM) is proposed, which simplifies the process of network structure selection and reduces time expenses to adjust their parameters. Application of the proposed algorithm is illustrated by solving two-dimensional Poisson equation.
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Holden, Sean B., and Mahesan Niranjan. "Average-Case Learning Curves for Radial Basis Function Networks." Neural Computation 9, no. 2 (February 1, 1997): 441–60. http://dx.doi.org/10.1162/neco.1997.9.2.441.

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The application of statistical physics to the study of the learning curves of feedforward connectionist networks has to date been concerned mostly with perceptron-like networks. Recent work has extended the theory to networks such as committee machines and parity machines, and an important direction for current and future research is the extension of this body of theory to further connectionist networks. In this article, we use this formalism to investigate the learning curves of gaussian radial basis function networks (RBFNs) having fixed basis functions. (These networks have also been called generalized linear regression models.) We address the problem of learning linear and nonlinear, realizable and unrealizable, target rules from noise-free training examples using a stochastic training algorithm. Expressions for the generalization error, defined as the expected error for a network with a given set of parameters, are derived for general gaussian RBFNs, for which all parameters, including centers and spread parameters, are adaptable. Specializing to the case of RBFNs with fixed basis functions (basis functions having parameters chosen without reference to the training examples), we then study the learning curves for these networks in the limit of high temperature.
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HUANG, DE-SHUANG. "RADIAL BASIS PROBABILISTIC NEURAL NETWORKS: MODEL AND APPLICATION." International Journal of Pattern Recognition and Artificial Intelligence 13, no. 07 (November 1999): 1083–101. http://dx.doi.org/10.1142/s0218001499000604.

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This paper investigates the capabilities of radial basis function networks (RBFN) and kernel neural networks (KNN), i.e. a specific probabilistic neural networks (PNN), and studies their similarities and differences. In order to avoid the huge amount of hidden units of the KNNs (or PNNs) and reduce the training time for the RBFNs, this paper proposes a new feedforward neural network model referred to as radial basis probabilistic neural network (RBPNN). This new network model inherits the merits of the two old odels to a great extent, and avoids their defects in some ways. Finally, we apply this new RBPNN to the recognition of one-dimensional cross-images of radar targets (five kinds of aircrafts), and the experimental results are given and discussed.
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Gil Pita, R., R. Vicen, M. Rosa, M. P. Jarabo, P. Vera, and J. Curpian. "Ultrasonic flaw detection using radial basis function networks (RBFNs)." Ultrasonics 42, no. 1-9 (April 2004): 361–65. http://dx.doi.org/10.1016/j.ultras.2003.11.018.

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Joug, Shian Ming, Hsuan Ming Feng, and Dong Hui Guo. "Self-Tuning RBFNs Mobile Robot Systems through Bacterial Foraging Particle Swarm Optimization Learning Algorithm." Applied Mechanics and Materials 284-287 (January 2013): 2128–36. http://dx.doi.org/10.4028/www.scientific.net/amm.284-287.2128.

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A radial basis function neural networks (RBFNs) mobile robot control system is automatically developed with the image processing and learned by the bacterial foraging particle swarm optimization (BFPSO) algorithm in this paper. The image-based architecture of robot model is self-generated to travel the routing path in the dynamical and complicated environments. The visible omni-directional image sensors capture the surrounding environment to represent the behavior model of the mobile robot system. Three parameterize RBFNs model with the centers and spreads of each radial basis function, and the connection weights to solve the mobile robot path traveling and routing problems. Several free parameters of radial basis functions can be automatically tuned by the direct of the specified fitness function. In additional, the proper number of radial basis functions of the constructed RBFNs can be chosen by the defined fitness function which takes this factor into account. The desired multiple objectives of the RBFNs control system are proposed to simultaneously approach the shorter path and avoid the unexpected obstacles. Evaluations of PSO and BFPSO show that the developed RBFNs robot systems skip the obstacles and efficiently achieve the desired targets as soon as possible.
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Dash, Ch Sanjeev Kumar, Ajit Kumar Behera, Satchidananda Dehuri, and Sung-Bae Cho. "Radial basis function neural networks: a topical state-of-the-art survey." Open Computer Science 6, no. 1 (May 2, 2016): 33–63. http://dx.doi.org/10.1515/comp-2016-0005.

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AbstractRadial basis function networks (RBFNs) have gained widespread appeal amongst researchers and have shown good performance in a variety of application domains. They have potential for hybridization and demonstrate some interesting emergent behaviors. This paper aims to offer a compendious and sensible survey on RBF networks. The advantages they offer, such as fast training and global approximation capability with local responses, are attracting many researchers to use them in diversified fields. The overall algorithmic development of RBF networks by giving special focus on their learning methods, novel kernels, and fine tuning of kernel parameters have been discussed. In addition, we have considered the recent research work on optimization of multi-criterions in RBF networks and a range of indicative application areas along with some open source RBFN tools.
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MAYORGA, RENÉ V., and JONATHAN CARRERA. "A RADIAL BASIS FUNCTION NETWORK APPROACH FOR THE COMPUTATION OF INVERSE CONTINUOUS TIME VARIANT FUNCTIONS." International Journal of Neural Systems 17, no. 03 (June 2007): 149–60. http://dx.doi.org/10.1142/s0129065707001020.

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This Paper presents an efficient approach for the fast computation of inverse continuous time variant functions with the proper use of Radial Basis Function Networks (RBFNs). The approach is based on implementing RBFNs for computing inverse continuous time variant functions via an overall damped least squares solution that includes a novel null space vector for singularities prevention. The singularities avoidance null space vector is derived from developing a sufficiency condition for singularities prevention that conduces to establish some characterizing matrices and an associated performance index.
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HUANG, DE-SHUANG. "APPLICATION OF GENERALIZED RADIAL BASIS FUNCTION NETWORKS TO RECOGNITION OF RADAR TARGETS." International Journal of Pattern Recognition and Artificial Intelligence 13, no. 06 (September 1999): 945–62. http://dx.doi.org/10.1142/s0218001499000525.

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This paper extends general radial basis function networks (RBFN) with Gaussian kernel functions to generalized radial basis function networks (GRBFN) with Parzen window functions, and discusses applying the GRBFNs to recognition of radar targets. The equivalence between the RBFN classifiers (RBFNC) with outer-supervised signals of 0 or 1 and the estimate of Parzen windowed probabilistic density is proved. It is pointed out that the I/O functions of the hidden units in the RBFNC can be extended to general Parzen window functions (or called as potential functions). We present using recursive least square-backpropagation (RLS–BP) learning algorithm to train the GRBFNCs to classify five types of radar targets by means of their one-dimensional cross profiles. The concepts about the rate of recognition and confidence in the process of testing classification performance of the GRBFNCs are introduced. Six generalized kernel functions such as Gaussian, Double-Exponential, Triangle, Hyperbolic, Sinc and Cauchy, are used as the hidden I/O functions of the RBFNCs, and the classification performance of corresponding GRBFNCs for classifying one-dimensional cross profiles of radar targets is discussed.
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Dash, Ch Sanjeev Kumar, Ajit Kumar Behera, Satchidananda Dehuri, and Sung-Bae Cho. "Differential Evolution-Based Optimization of Kernel Parameters in Radial Basis Function Networks for Classification." International Journal of Applied Evolutionary Computation 4, no. 1 (January 2013): 56–80. http://dx.doi.org/10.4018/jaec.2013010104.

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In this paper a two phases learning algorithm with a modified kernel for radial basis function neural networks is proposed for classification. In phase one a new meta-heuristic approach differential evolution is used to reveal the parameters of the modified kernel. The second phase focuses on optimization of weights for learning the networks. Further, a predefined set of basis functions is taken for empirical analysis of which basis function is better for which kind of domain. The simulation result shows that the proposed learning mechanism is evidently producing better classification accuracy vis-à-vis radial basis function neural networks (RBFNs) and genetic algorithm-radial basis function (GA-RBF) neural networks.
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Dissertations / Theses on the topic "Radial basis function networks (RBFNs)"

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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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Books on the topic "Radial basis function networks (RBFNs)"

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Howlett, Robert J., and Lakhmi C. Jain, eds. Radial Basis Function Networks 2. Heidelberg: Physica-Verlag HD, 2001. http://dx.doi.org/10.1007/978-3-7908-1826-0.

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1931-, Haykin Simon S., ed. Regularized radial basis function networks: Theory and applications. New York: John Wiley, 2001.

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Billings, S. A. Adaptive noise cancellation using recurrent radial basis function networks. Sheffield: University of Sheffield, Dept. of Automatic Control and Systems Engineering, 1993.

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Fung, Chi Fung. On-line supervised adaptive training using radial basis function networks. Sheffield: Dept. of Automatic Control and Systems Engineering, University of Sheffield, 1994.

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P, Saratchandran, and Li Yan 1972-, eds. Fully tuned radial basis function neural networks for flight control. Boston: Kluwer Academic, 2002.

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Sundararajan, N. Fully Tuned Radial Basis Function Neural Networks for Flight Control. Boston, MA: Springer US, 2002.

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Sundararajan, N., P. Saratchandran, and Yan Li. Fully Tuned Radial Basis Function Neural Networks for Flight Control. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4757-5286-1.

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Billings, S. A. Dual-orthogonal radial basis function networks for nonlinear time series prediction. Sheffield: University of Sheffield, Dept. of Automatic Control and Systems Engineering, 1996.

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Luo, W. Structure selective updating for nonlinear models and radial basis function neural networks. Sheffield: University of Sheffield, Dept. of Automatic Control and Systems Engineering, 1995.

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Zhu, Q. M. Fast orthogonal identification of nonlinear stochastic models and radial basis function neural networks. Sheffield: University of Sheffield, Dept. of Automatic Control and Systems Engineering, 1994.

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Book chapters on the topic "Radial basis function networks (RBFNs)"

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Sundararajan, N., P. Saratchandran, and Yan Li. "Indirect Adaptive Control Using Fully Tuned RBFN." In Fully Tuned Radial Basis Function Neural Networks for Flight Control, 69–80. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4757-5286-1_4.

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Sundararajan, N., P. Saratchandran, and Yan Li. "Nonlinear System Identification Using Lyapunov-Based Fully Tuned RBFN." In Fully Tuned Radial Basis Function Neural Networks for Flight Control, 29–45. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4757-5286-1_2.

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Sundararajan, N., P. Saratchandran, and Yan Li. "Direct Adaptive Neuro Flight Controller Using Fully Tuned RBFN." In Fully Tuned Radial Basis Function Neural Networks for Flight Control, 85–94. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4757-5286-1_5.

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da Silva, Ivan Nunes, Danilo Hernane Spatti, Rogerio Andrade Flauzino, Luisa Helena Bartocci Liboni, and Silas Franco dos Reis Alves. "Radial Basis Function Networks." In Artificial Neural Networks, 117–38. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-43162-8_6.

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Du, Ke-Lin, and M. N. S. Swamy. "Radial Basis Function Networks." In Neural Networks and Statistical Learning, 315–49. London: Springer London, 2019. http://dx.doi.org/10.1007/978-1-4471-7452-3_11.

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Kruse, Rudolf, Christian Borgelt, Christian Braune, Sanaz Mostaghim, and Matthias Steinbrecher. "Radial Basis Function Networks." In Texts in Computer Science, 93–112. London: Springer London, 2016. http://dx.doi.org/10.1007/978-1-4471-7296-3_6.

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Kruse, Rudolf, Christian Borgelt, Frank Klawonn, Christian Moewes, Matthias Steinbrecher, and Pascal Held. "Radial Basis Function Networks." In Texts in Computer Science, 83–103. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-5013-8_6.

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Buhmann, M. D. "Radial Basis Function Networks." In Encyclopedia of Machine Learning and Data Mining, 1–6. Boston, MA: Springer US, 2016. http://dx.doi.org/10.1007/978-1-4899-7502-7_698-1.

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Buhmann, Martin D. "Radial Basis Function Networks." In Encyclopedia of Machine Learning and Data Mining, 1049–54. Boston, MA: Springer US, 2017. http://dx.doi.org/10.1007/978-1-4899-7687-1_698.

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Aggarwal, Charu C. "Radial Basis Function Networks." In Neural Networks and Deep Learning, 217–33. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-94463-0_5.

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Conference papers on the topic "Radial basis function networks (RBFNs)"

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Luo, Run, Shifa Wu, Xinyu Wei, and Fuyu Zhao. "Identification Modeling of Accelerator Driven System Based on Growing and Pruning Radial Basis Function Network." In 2016 24th International Conference on Nuclear Engineering. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/icone24-60328.

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An identification method based on growing and pruning radial basis function network (GAP-RBFN) is presented for modeling an accelerator driven system (ADS). Compared with traditional neural networks, GAP-RBFN could automatically adjust the number of hidden neurons to find a suitable network structure by using growing and pruning strategies. In addition, an extended Kalman filter (EKF) algorithm is adopted to update network parameters of neurons in GAP-RBFN, which has a rapid convergence speed during the training process. A numerical calculation code named ARTAP (ADS Reactor Transient Analysis Program) is used to generate data for training GAP-RBFN. After GAP-RBFN is trained by the data, an identification model for ADS is established. The simulation results obtained from the GAP-RBFN model are compared with those obtained from a recurrent neural network (RNN) model. It is shown that the GAP-RBFN model not only has higher prediction accuracy than the RNN model, but also has faster computation speed than the numerical calculation code. Owing to its accuracy, simplicity and fast computation speed, the proposed GAP-RBFN method can be used to model the ADS reactor.
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Strömberg, Niclas. "Reliability Based Design Optimization by Using a SLP Approach and Radial Basis Function Networks." In ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/detc2016-59522.

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In this paper reliability based design optimization by using radial basis function networks (RBFN) as surrogate models is presented. The RBFN are treated as regression models. By taking the center points equal to the sampling points an interpolation is obtained. The bias of the network is taken to be known a priori or posteriori. In the latter case, the well-known orthogonality constraint between the weights of the RBFN and the polynomial basis functions of the bias is adopted. The optimization is performed by using a first order reliability method (FORM)-based sequential linear programming (SLP) approach, where the Taylor expansions are generated in intermediate variables defined by the iso-probabilistic transformation. In addition, the reliability constraints are expanded at the most probable points which are found by using Newton’s method. The Newton algorithm is derived by proposing an in-exact Jacobian. In such manner, a FORM-based LP-formulation in the standard normal space of problems with non-Gaussian variables is suggested. The solution from the LP-problem is mapped back to the physical space and the suggested procedure continues in a sequence until convergence is reached. This is implemented for five different distributions: normal, lognormal, Gumbel, gamma and Weibull. It is also presented how the FORM-based SLP approach can be corrected by using second order reliability methods (SORM) and Monte Carlo simulations. In particular, the SORM approach of Hohenbichler is studied. The outlined methodology is both efficient and robust. This is demonstrated by solving established benchmarks as well as finite element problems.
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Sui, Wenbo, and Carrie M. Hall. "SCR Control System Design Based on On-Line Radial Basis Function and Backpropagation Neural Networks." In ASME 2017 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/dscc2017-5095.

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Because of its high NOx reduction efficiency, selective catalyst reduction (SCR) has become an indispensable part of diesel vehicle aftertreatment. This paper presents a control strategy for SCR systems that is based on an on-line radial basis function neural network (RBFNN) and an on-line backpropagation neural network (BPNN). In this control structure, the radial basis function neural network is employed as an estimator to provide Jacobian information for the controller; and the backpropagation neural network is utilized as a controller, which dictates the appropriate urea-solution to be injected into the SCR system. This design is tested by simulations based in Gamma Technologies software (GT-ISE) as well as MATLAB Simulink. The results show that the RBF-BPNN control technique achieves a 1–5 % higher NOx reduction efficiency than a PID controller.
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Amouzgar, Kaveh, and Niclas Stromberg. "An Approach Towards Generating Surrogate Models by Using RBFN With a Priori Bias." In ASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/detc2014-34948.

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In this paper, an approach to generate surrogate models constructed by radial basis function networks (RBFN) with a priori bias is presented. RBFN as a weighted combination of radial basis functions only, might become singular and no interpolation is found. The standard approach to avoid this is to add a polynomial bias, where the bias is defined by imposing orthogonality conditions between the weights of the radial basis functions and the polynomial basis functions. Here, in the proposed a priori approach, the regression coefficients of the polynomial bias are simply calculated by using the normal equation without any need of the extra orthogonality prerequisite. In addition to the simplicity of this approach, the method has also proven to predict the actual functions more accurately compared to the RBFN with a posteriori bias. Several test functions, including Rosenbrock, Branin-Hoo, Goldstein-Price functions and two mathematical functions (one large scale), are used to evaluate the performance of the proposed method by conducting a comparison study and error analysis between the RBFN with a priori and a posteriori known biases. Furthermore, the aforementioned approaches are applied to an engineering design problem, that is modeling of the material properties of a three phase spherical graphite iron (SGI). The corresponding surrogate models are presented and compared.
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Almaita, Eyad, and Johnson A. Asumadu. "Dynamic harmonic identification in converter waveforms using radial basis function neural networks (RBFNN) and p-q power theory." In 2011 IEEE International Conference on Industrial Technology (ICIT 2011). IEEE, 2011. http://dx.doi.org/10.1109/icit.2011.5754360.

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Almaita, Eyad, and Johnson A. Asumadu. "Harmonic content extraction in converter waveforms using radial basis function neural networks (RBFNN) and p-q power theory." In 2011 IEEE Power and Energy Conference at Illinois (PECI). IEEE, 2011. http://dx.doi.org/10.1109/peci.2011.5740486.

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Perez, Carlos, and Juan De Dios Calderon. "Comparison Between Feed-Forward Back-Propagation and Radial Basis Functions Networks for Roughness Modeling in Face-Milling of Aluminum." In ASME 2011 International Mechanical Engineering Congress and Exposition. ASMEDC, 2011. http://dx.doi.org/10.1115/imece2011-63364.

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The technology of the cutting process has evolved substantially in terms of materials, tools and machines; however it is a necessity to develop models for control and optimization of the cutting processes because nowadays industry relies mainly on empirical data and heuristic solutions provided by shop-floor experts. Due to the complex relationship between the variables of the cutting process, application of artificial intelligence approaches is wide feasible as modeling technique and functional for controller development. This work, presents a design of experiments, data analysis and model comparison for surface roughness prediction in face-milling of aluminum 6061-T6 considering tool spindle speed, feed rate, depth of cut and entry angle of the cutting insert as input variables. Measurements of average roughness are performed, then acquired data are preprocessed using the Principal Component Analysis (PCA) and Response Surface Methodologies (RSM). Once the proper variable arrangement is defined, a comparison between Feed Forward Back Propagation Neural Network (FFBPNN) and Radial Basis Function Neural Network (RBFNN) is performed based on the correlation coefficient between predicted and measured data. Results showed that Neural Network arrays have better prediction behavior than those based on RSM.
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Amouzgar, Kaveh, Asim Rashid, and Niclas Stromberg. "Multi-Objective Optimization of a Disc Brake System by Using SPEA2 and RBFN." In ASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/detc2013-12809.

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Many engineering design optimization problems involve multiple conflicting objectives, which today often are obtained by computational expensive finite element simulations. Evolutionary multi-objective optimization (EMO) methods based on surrogate modeling is one approach of solving this class of problems. In this paper, multi-objective optimization of a disc brake system to a heavy truck by using EMO and radial basis function networks (RBFN) is presented. Three conflicting objectives are considered. These are: 1) minimizing the maximum temperature of the disc brake, 2) maximizing the brake energy of the system and 3) minimizing the mass of the back plate of the brake pad. An iterative Latin hypercube sampling method is used to construct the design of experiments (DoE) for the design variables. Next, thermo-mechanical finite element analysis of the disc brake, including frictional heating between the pad and the disc, is performed in order to determine the values of the first two objectives for the DoE. Surrogate models for the maximum temperature and the brake energy are created using RBFN with polynomial biases. Different radial basis functions are compared using statistical errors and cross validation errors (PRESS) to evaluate the accuracy of the surrogate models and to select the most accurate radial basis function. The multi-objective optimization problem is then solved by employing EMO using the strength Pareto evolutionary algorithm (SPEA2). Finally, the Pareto fronts generated by the proposed methodology are presented and discussed.
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Lima, Jeferson J., Rodrigo T. Rocha, Frederic C. Janzen, Angelo M. Tusset, Dailhane G. Bassinello, and Jose M. Balthazar. "Position Control of a Manipulator Robotic Arm Considering Flexible Joints Driven by a DC Motor and a Controlled Torque by a MR-Brake." In ASME 2016 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/imece2016-66235.

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This paper presents a two-degree-of-freedom robotic arm design with flexible joints driven by a DC Motor and controlled by a Magnetorheological (MR) Brake, considering a feedback control. The MR Brake is used to provide adjustable constraints in motion of the manipulator and compensate overshoot by interactions between the robot’s links and flexible joints of the motor drive mechanism. The torque of the MR Brake is obtained by the Radial Basis Function Neural Networks (RBFNN), which is a widely used class of neural networks for prediction or approximation of function. The RBFNN provides the nonlinear curve of hysteresis of MR brake to use torque. Two controllers were proposed to control the manipulator. The first one is obtained by feedback linearization control with the objective to remove the non-dependent terms of the state space equation. The second one is the feedback control obtained using the State-Dependent Riccati Equation (SDRE) with the objective of controlling the position of the manipulator and the torque applied on the MR brake. The numerical simulation results showed that the proposed control using both signal feedback linearization control and a feedback control signal by a DC Motor and MR Brake is effective to control the flexible joint manipulators.
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Balcilar, Muhammet, Ahmet Selim Dalkilic¸, and Somchai Wongwises. "Determination of Condensation Heat Transfer Characteristics of R134A by Means of Artificial Intelligence Method." In ASME 2010 International Mechanical Engineering Congress and Exposition. ASMEDC, 2010. http://dx.doi.org/10.1115/imece2010-38453.

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The present study investigates the best artificial neural network (ANN) approach to estimate the measured convective heat transfer coefficient of R134a flowing downward inside a vertical smooth copper tube having an inner diameter of 8.1mm and a length of 500mm during annular flow numerically. R134a and water are used as working fluids in the tube side and annular side of a double tube heat exchanger, respectively. Experimental data, used as the ANN training set, came from intube condensation tests including three different mass fluxes of R134a such as 260, 340 and 456 kg m−2s−1, two different saturation temperatures of R134a such as 40 and 50 °C and heat fluxes ranging from 10.83 to 50.89 kW m−2. Accuracy of the dataset was proven in many papers in the literature. The quality of the refrigerant in the test section is calculated considering the temperature and pressure obtained from the experiment. The pressure drop across the test section is directly measured by a differential pressure transducer. Measured values of test section such as mass flux, heat flux, the temperature difference between the tube wall and saturation temperature, average vapor quality are assigned as input of the ANNs, while the experimental condensation heat transfer coefficient and measured pressure drop are specified as the output in the analysis. The artificial neural network (ANN) methods of multi-layer perceptron (MLP), radial basis networks (RBFN), generalized regression neural network (GRNN) and adaptive neuro-fuzzy inference system (ANFIS) were used to decide the best approach for modeling condensation heat transfer characteristics of R134a. 183 data points obtained in the experiments are divided into two sets randomly. Sets of test and training/validation are including 33 and 120/30 data points respectively. In training phase, 5-fold cross validation is used for determine the best value of ANNs control parameters. The ANNs performances were measured by mean relative error criteria with the usage of unknown test sets. The performance of the method of multi layer perceptron (MLP) with 5-13-1 architecture and radial basis function networks (RBFN) with the spread coefficient (sp) of 40000 were found to be superior to other methods and architectures by means of satisfactory results with their deviations within the range of ±0.58% for the estimated condensation heat transfer coefficient and ±1.74% for the estimated pressure drop respectively.
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Reports on the topic "Radial basis function networks (RBFNs)"

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Computational experience with radial basis function networks. Gaithersburg, MD: National Institute of Standards and Technology, 1993. http://dx.doi.org/10.6028/nist.ir.5168.

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