Relevant bibliographies by topics / Radial basis function (RBF) model

Academic literature on the topic 'Radial basis function (RBF) model'

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Journal articles on the topic "Radial basis function (RBF) model"

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Hu, Shan-Feng, Hong-Bin Zhu, and Lei Zhao. "Radial basis function and its application in tourism management." Modern Physics Letters B 32, no. 12n13 (May 10, 2018): 1840054. http://dx.doi.org/10.1142/s0217984918400547.

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In this work, several applications and the performances of the radial basis function (RBF) are briefly reviewed at first. After that, the binomial function combined with three different RBFs including the multiquadric (MQ), inverse quadric (IQ) and inverse multiquadric (IMQ) distributions are adopted to model the tourism data of Huangshan in China. Simulation results showed that all the models match very well with the sample data. It is found that among the three models, the IMQ-RBF model is more suitable for forecasting the tourist flow.
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Wu, Yue, Hui Wang, Biaobiao Zhang, and K. L. Du. "Using Radial Basis Function Networks for Function Approximation and Classification." ISRN Applied Mathematics 2012 (March 6, 2012): 1–34. http://dx.doi.org/10.5402/2012/324194.

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The radial basis function (RBF) network has its foundation in the conventional approximation theory. It has the capability of universal approximation. The RBF network is a popular alternative to the well-known multilayer perceptron (MLP), since it has a simpler structure and a much faster training process. In this paper, we give a comprehensive survey on the RBF network and its learning. Many aspects associated with the RBF network, such as network structure, universal approimation capability, radial basis functions, RBF network learning, structure optimization, normalized RBF networks, application to dynamic system modeling, and nonlinear complex-valued signal processing, are described. We also compare the features and capability of the two models.
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Holmes, C. C., and B. K. Mallick. "Bayesian Radial Basis Functions of Variable Dimension." Neural Computation 10, no. 5 (July 1, 1998): 1217–33. http://dx.doi.org/10.1162/089976698300017421.

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A Bayesian framework for the analysis of radial basis functions (RBF) is proposed that accommodates uncertainty in the dimension of the model. A distribution is defined over the space of all RBF models of a given basis function, and posterior densities are computed using reversible jump Markov chain Monte Carlo samplers (Green, 1995). This alleviates the need to select the architecture during the modeling process. The resulting networks are shown to adjust their size to the complexity of the data.
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Shao, Changpeng. "Quantum speedup of training radial basis function networks." Quantum Information and Computation 19, no. 7&8 (June 2019): 609–25. http://dx.doi.org/10.26421/qic19.7-8-6.

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Radial basis function (RBF) network is a simple but useful neural network model that contains wide applications in machine learning. The training of an RBF network reduces to solve a linear system, which is time consuming classically. Based on HHL algorithm, we propose two quantum algorithms to train RBF networks. To apply the HHL algorithm, we choose using the Hamiltonian simulation algorithm proposed in [P. Rebentrost, A. Steffens, I. Marvian and S. Lloyd, Phys. Rev. A 97, 012327, 2018]. However, to use this result, an oracle to query the entries of the matrix of the network should be constructed. We apply the amplitude estimation technique to build this oracle. The final results indicate that if the centers of the RBF network are the training samples, then the quantum computer achieves exponential speedup at the number and the dimension of training samples over the classical computer; if the centers are determined by the K-means algorithm, then the quantum computer achieves quadratic speedup at the number of samples and exponential speedup at the dimension of samples.
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El Shafie, Amr H., A. El-Shafie, A. Almukhtar, Mohd R. Taha, Hasan G. El Mazoghi, and A. Shehata. "Radial basis function neural networks for reliably forecasting rainfall." Journal of Water and Climate Change 3, no. 2 (June 1, 2012): 125–38. http://dx.doi.org/10.2166/wcc.2012.017.

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Rainfall forecasting is an interesting task especially in a modern city facing the problem of global warming; in addition rainfall is a necessary input for the analysis and design of hydrologic systems. Most rainfall real-time forecasting models are based on conceptual models simulating the complex hydrological process under climate variability. As there are a lot of variables and parameters with uncertainties and non-linear relationships, the calibration of conceptual or physically based models is often a difficult and time-consuming procedure. Simpler artificial neural network (ANN) forecasts may therefore seem attractive as an alternative model. The present research demonstrates the application of the radial basis function neural network (RBFNN) to rainfall forecasting for Alexandria City, Egypt. A significant feature of the input construction of the RBF network is based on the use of the average 10 year rainfall in each decade to forecast the next year. The results show the capability of the RBF network in forecasting the yearly rainfall and two highest rainfall monsoon months, January and December, compared with other statistical models. Based on these results, the use of the RBF model can be recommended as a viable alternative for forecasting the rainfall based on historical rainfall recorded data.
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Lin, W., M. H. Wu, and S. Duan. "Engine Test Data Modelling by Evolutionary Radial Basis Function Networks." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 217, no. 6 (June 1, 2003): 489–97. http://dx.doi.org/10.1243/095440703766518113.

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The engine test bed is introduced briefly and the importance of modelling for the engine test is discussed. The application of combining radial basis function (RBF) networks and a real-coded genetic algorithm (RCGA) to create the model is described for the engine test. Finally, the experimental results are analysed and it is shown that the proposed approach combining RCGA and RBF models is well suited for the engine test data modelling task.
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Park, Byungkyu, Carroll J. Messer, and Thomas Urbanik. "Short-Term Freeway Traffic Volume Forecasting Using Radial Basis Function Neural Network." Transportation Research Record: Journal of the Transportation Research Board 1651, no. 1 (January 1998): 39–47. http://dx.doi.org/10.3141/1651-06.

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A radial basis function (RBF) neural network has recently been applied to time-series forecasting. The test results of an RBF neural network in forecasting short-term freeway traffic volumes are provided. Real observations of freeway traffic volumes from the San Antonio TransGuide System have been used in these experiments. For comparison of forecasting performances, Taylor series, exponential smoothing method (ESM), double exponential smoothing method, and backpropagation neural network were also designed and tested. The RBF neural network model provided the best performance and required less computational time than BPN. It seems that RBF and ESM can be a viable forecasting routine for advanced traffic management systems. There are some tradeoffs between RBF and ESM. Although the performance of ESM is inferior to RBF, the former does not need a complicated training process or historic database, and vice versa. However, even in the best performance case, 35 percent of the forecast traffic volumes showed 10 percent or more percentage errors. This means that we cannot heavily depend on the forecast traffic volumes as long as we are utilizing the models tested. Further work is needed to provide a more reliable traffic forecasting model.
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Yang, Qing Wei, Nai Chao Wang, and Ma Lin. "Application of Radial Basis Function Neural Network to Support Concept Evaluation." Advanced Materials Research 472-475 (February 2012): 1926–31. http://dx.doi.org/10.4028/www.scientific.net/amr.472-475.1926.

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In order to solve the problem that how to evaluate the complex system support concept, an evaluation method based on Radial Basis Function (RBF) neural network model was presented. Through researching the support system overall design characteristics and elements of support, on this basis, evaluation parameters of support concept were abstracted. Support concept evaluation model based on RBF was established and a mature and stable RBF neural network was trained to calculate the comprehensive evaluation value for support concept. Finally, the further demonstration and verification of the method are given through specific case application and compared with the result for evaluation results of data envelopment analysis (DEA) model.
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Ku, Cheng-Yu, Chih-Yu Liu, and Frank T. C. Tsai. "A Novel Radial Basis Function Approach for Infiltration-Induced Landslides in Unsaturated Soils." Water 14, no. 7 (March 25, 2022): 1036. http://dx.doi.org/10.3390/w14071036.

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In this article, the modeling of infiltration--induced landslides, in unsaturated soils using the radial basis function (RBF) method, is presented. A novel approach based on the RBF method is proposed to deal with the nonlinear hydrological process in the unsaturated zone. The RBF is first adopted for curve fitting to build the representation of the soil water characteristic curve (SWCC) that corresponds to the best estimate of the relationship between volumetric water content and matric suction. The meshless method with the RBF is then applied to solve the nonlinear Richards equation with the infiltration boundary conditions. Additionally, the fictitious time integration method is adopted in the meshless method with the RBF for tackling the nonlinearity. To model the stability of the landslide, the stability analysis of infinite slope coupled with the nonlinear Richards equation considering the fluctuation of transient pore water pressure is developed. The validation of the proposed approach is accomplished by comparing with exact solutions. The comparative analysis of the factor of safety using the Gardner model, the van Genuchten model and the proposed RBF model is provided. Results illustrate that the RBF is advantageous for reconstructing the SWCC with better estimation of the relationship than conventional parametric Gardner and van Genuchten models. We also found that the computed safety factors significantly depend on the representation of the SWCC. Finally, the stability of landslides is highly affected by matric potential in unsaturated soils during the infiltration process.
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Chen, Gan. "Multimedia Security Situation Prediction Based on Optimization of Radial Basis Function Neural Network Algorithm." Computational Intelligence and Neuroscience 2022 (April 8, 2022): 1–8. http://dx.doi.org/10.1155/2022/6314262.

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Aiming at the problem of prediction accuracy in network situation awareness, a network security situation prediction method based on a generalized radial basis function (RBF) neural network is proposed. This method uses the K-means clustering algorithm to determine the data center and expansion function of the RBF and uses the least-mean-square algorithm to adjust the weights to obtain the nonlinear mapping relationship between the situation value before and after the situation and carry out the situation prediction. Simulation experiments show that this method can obtain situation prediction results more accurately and improve the active security protection of network security. Compared with the PSO-RBF model, AFSA-RBF model, and IAFSA-RBF model, the maximum relative error and minimum relative error of the IAFSA-PSO-RBF model are reduced by 14.27%, 8.91%, and 32.98%, respectively, and the minimum relative error is reduced by 1.69%, 12.97%, and 0.61%, respectively. This shows that the IAFSA-PSO-RBF model has reduced the prediction error interval, and the average relative error is 5%. Compared with the other three models, the accuracy rate is improved by more than 5%, and it has met the requirements for the prediction of the network security situation.
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Dissertations / Theses on the topic "Radial basis function (RBF) model"

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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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Made available in DSpace on 2014-06-12T15:52:45Z (GMT). No. of bitstreams: 2 arquivo4692_1.pdf: 1118830 bytes, checksum: 96894dd8a22373c59d67d3b286b6c902 (MD5) license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5) Previous issue date: 2003
Um 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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Amouzgar, Kaveh. "Metamodel based multi-objective optimization." Licentiate thesis, Tekniska Högskolan, Högskolan i Jönköping, JTH. Forskningsmiljö Produktutveckling - Simulering och optimering, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-28432.

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As a result of the increase in accessibility of computational resources and the increase in the power of the computers during the last two decades, designers are able to create computer models to simulate the behavior of a complex products. To address global competitiveness, companies are forced to optimize their designs and products. Optimizing the design needs several runs of computationally expensive simulation models. Therefore, using metamodels as an efficient and sufficiently accurate approximate of the simulation model is necessary. Radial basis functions (RBF) is one of the several metamodeling methods that can be found in the literature. The established approach is to add a bias to RBF in order to obtain a robust performance. The a posteriori bias is considered to be unknown at the beginning and it is defined by imposing extra orthogonality constraints. In this thesis, a new approach in constructing RBF with the bias to be set a priori by using the normal equation is proposed. The performance of the suggested approach is compared to the classic RBF with a posteriori bias. Another comprehensive comparison study by including several modeling criteria, such as problem dimension, sampling technique and size of samples is conducted. The studies demonstrate that the suggested approach with a priori bias is in general as good as the performance of RBF with a posteriori bias. Using the a priori RBF, it is clear that the global response is modeled with the bias and that the details are captured with radial basis functions. Multi-objective optimization and the approaches used in solving such problems are briefly described in this thesis. One of the methods that proved to be efficient in solving multi-objective optimization problems (MOOP) is the strength Pareto evolutionary algorithm (SPEA2). Multi-objective optimization of a disc brake system of a heavy truck by using SPEA2 and RBF with a priori bias is performed. As a result, the possibility to reduce the weight of the system without extensive compromise in other objectives is found. Multi-objective optimization of material model parameters of an adhesive layer with the aim of improving the results of a previous study is implemented. The result of the original study is improved and a clear insight into the nature of the problem is revealed.
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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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Du, Toit Wilna. "Radial basis function interpolation." Thesis, Stellenbosch : Stellenbosch University, 2008. http://hdl.handle.net/10019.1/2002.

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Thesis (MSc (Applied Mathematics))--Stellenbosch University, 2008.
A popular method for interpolating multidimensional scattered data is using radial basis functions. In this thesis we present the basic theory of radial basis function interpolation and also regard the solvability and stability of the method. Solving the interpolant directly has a high computational cost for large datasets, hence using numerical methods to approximate the interpolant is necessary. We consider some recent numerical algorithms. Software to implement radial basis function interpolation and to display the 3D interpolants obtained, is developed. We present results obtained from using our implementation for radial basis functions on GIS and 3D face data as well as an image warping application.
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Shcherbakov, Victor. "Localised Radial Basis Function Methods for Partial Differential Equations." Doctoral thesis, Uppsala universitet, Avdelningen för beräkningsvetenskap, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-332715.

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Radial basis function methods exhibit several very attractive properties such as a high order convergence of the approximated solution and flexibility to the domain geometry. However the method in its classical formulation becomes impractical for problems with relatively large numbers of degrees of freedom due to the ill-conditioning and dense structure of coefficient matrix. To overcome the latter issue we employ a localisation technique, namely a partition of unity method, while the former issue was previously addressed by several authors and was of less concern in this thesis. In this thesis we develop radial basis function partition of unity methods for partial differential equations arising in financial mathematics and glaciology. In the applications of financial mathematics we focus on pricing multi-asset equity and credit derivatives whose models involve several stochastic factors. We demonstrate that localised radial basis function methods are very effective and well-suited for financial applications thanks to the high order approximation properties that allow for the reduction of storage and computational requirements, which is crucial in multi-dimensional problems to cope with the curse of dimensionality. In the glaciology application we in the first place make use of the meshfree nature of the methods and their flexibility with respect to the irregular geometries of ice sheets and glaciers. Also, we exploit the fact that radial basis function methods are stated in strong form, which is advantageous for approximating velocity fields of non-Newtonian viscous liquids such as ice, since it allows to avoid a full coefficient matrix reassembly within the nonlinear iteration. In addition to the applied problems we develop a least squares radial basis function partition of unity method that is robust with respect to the node layout. The method allows for scaling to problem sizes of a few hundred thousand nodes without encountering the issue of large condition numbers of the coefficient matrix. This property is enabled by the possibility to control the coefficient matrix condition number by the rate of oversampling and the mode of refinement.
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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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Toratti, Luiz Otávio. "Design de campos vetoriais em volumes usando RBF." Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-22102018-170348/.

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Em Computação Gráfica, campos vetoriais possuem diversas aplicações desde a síntese e mapeamento de texturas à animações de fluidos, produzindo efeitos amplamente utilizados na indústria do entretenimento. Para produzir tais campos, é preferível o uso de ferramentas de design em vez de simulações numéricas não só devido ao menor custo computacional mas, principalmente, por prover liberdade ao artista ao sintetizar o campo de acordo com a sua necessidade. Atualmente, na literatura, existem bons métodos de design de campos vetoriais em superfícies de objetos tridimensionais porém, o design no interior desses objetos ainda é pouco estudado, principalmente quando o campo de interesse possui propriedades específicas. O objetivo deste trabalho é desenvolver uma técnica para sintetizar campos vetoriais, com características do movimento de fluidos incompressíveis, no interior de domínios. Em uma primeira etapa, o método consiste na interpolação dos vetores de controle, com uma certa propriedade desejada, em todo o domínio. Posteriormente, o campo obtido é modificado para respeitar a geometria do contorno.
Vector fields are important to an wide range of applications on the field of Computer Graphics, from the synthesis and mapping of textures to fluid animation, producing effects widely used on the entertainment industry. To produce such fields, design tools are prefered over numerical simulations not only for its lower computational cost, but mainly by providing freedom to the artist in the creation process. Nowadays, good methods of vector field design over surfaces exist in literature, however there is only a few studies on the synthesis of vector fields of the interior of objects and even fewer when specific properties of the field are required. This work presents a technique to synthesize vector fields with properties of imcompressible fluids motion in the interior of objects. On a first step, the method consists in interpolating control vectors with a certain desired property throughout the whole domain and later the resulting field is modified to properly fit the boundary geometry of the object.
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Wang, Cong. "Evaluation of a least-squares radial basis function approximation method for solving the Black-Scholes equation for option pricing." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-183042.

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Radial basis function (RBF) approximation, is a new extremely powerful tool that is promising for high-dimensional problems, such as those arising from pricing of basket options using the Black-Scholes partial differential equation. The main problem for RBF methods have been ill-conditioning as the RBF shape parameter becomes small, corresponding to flat RBFs. This thesis employs a recently developed method called the RBF-QR method to reduce computational cost by improving the conditioning, thereby allowing for the use of a wider range of shape parameter values. Numerical experiments for the one-dimensional case are presented  and a MATLAB implementation is provided. In our thesis, the RBF-QR method performs better  than the RBF-Direct method for small shape parameters. Using Chebyshev points, instead of a standard uniform distribution, can increase the accuracy through clustering of the nodes towards the boundary. The least squares formulation for RBF methods is preferable to the collocation approach because it can result in smaller errors  for the same number of basis functions.
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Stephanson, Matthew B. "An Adaptive, Black-Box Model Order Reduction Algorithm Using Radial Basis Functions." The Ohio State University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=osu1345226428.

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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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Books on the topic "Radial basis function (RBF) model"

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Liu, Jinkun. Radial Basis Function (RBF) Neural Network Control for Mechanical Systems: Design, Analysis and Matlab Simulation. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.

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Liu, Jinkun. Radial Basis Function (RBF) Neural Network Control for Mechanical Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-34816-7.

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Radial Basis Function Rbf Neural Network Control For Mechanical Systems Design Analysis And Matlab Simulation. Springer-Verlag Berlin and Heidelberg GmbH &, 2013.

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Book chapters on the topic "Radial basis function (RBF) model"

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Howell, A. J. "Face Recognition Using RBF Networks." In Radial Basis Function Networks 2, 103–41. Heidelberg: Physica-Verlag HD, 2001. http://dx.doi.org/10.1007/978-3-7908-1826-0_4.

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Hogan, J. M., M. Norris, and J. Diederich. "Classification of Facial Expressions with Domain Gaussian RBF Networks." In Radial Basis Function Networks 2, 143–65. Heidelberg: Physica-Verlag HD, 2001. http://dx.doi.org/10.1007/978-3-7908-1826-0_5.

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Biancolini, Marco Evangelos. "Optimization Workflows Assisted by RBF Surrogate Models." In Fast Radial Basis Functions for Engineering Applications, 257–87. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-75011-8_11.

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Fels, S. S. "Using Normalized RBF Networks to Map Hand Gestures to Speech." In Radial Basis Function Networks 2, 59–101. Heidelberg: Physica-Verlag HD, 2001. http://dx.doi.org/10.1007/978-3-7908-1826-0_3.

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Kestler, H. A., and F. Schwenker. "RBF Network Classification of ECGs as a Potential Marker for Sudden Cardiac Death." In Radial Basis Function Networks 2, 167–214. Heidelberg: Physica-Verlag HD, 2001. http://dx.doi.org/10.1007/978-3-7908-1826-0_6.

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Chen, Wen, Zhuo-Jia Fu, and C. S. Chen. "Boundary-Type RBF Collocation Methods." In Recent Advances in Radial Basis Function Collocation Methods, 51–87. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39572-7_4.

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Liu, Jinkun. "Backstepping Control with RBF." In Radial Basis Function (RBF) Neural Network Control for Mechanical Systems, 251–92. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34816-7_8.

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Hoffmann, Günther A. "Adaptive Transfer Functions in Radial Basis Function (RBF) Networks." In Computational Science - ICCS 2004, 682–86. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24687-9_102.

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Kindelan, Manuel, and Francisco Bernal. "Radial Basis Function (RBF) Solution of the Motz Problem." In Progress in Industrial Mathematics at ECMI 2008, 907–12. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12110-4_145.

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Liu, Jinkun. "Adaptive RBF Neural Network Control." In Radial Basis Function (RBF) Neural Network Control for Mechanical Systems, 71–112. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34816-7_4.

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Conference papers on the topic "Radial basis function (RBF) model"

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Fu, Zhuo-Jia. "Radial Basis Function Methods for Fractional Derivative Applications." In ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/detc2015-48016.

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In recent decades, the theoretical researches and experimental results show that fractional derivative model can be a powerful tool to describe the contaminant transport through complex porous media and the dynamic behaviors of real viscoelastic materials. Consequently, growing attention has been attracted to numerical solution of fractional derivative model. Radial basis function (RBF) meshless technique is one of the most popular and powerful numerical methods, which are mathematically simple, and avoid troublesome mesh generation for high-dimensional problems involving irregular or moving boundary. Recently, RBF-based meshless methods, such as the Boundary Particle Method and the Method of Approximate Particular Solutions, have been successfully applied to fractional derivative problems. The Boundary Particle Method is one of truly boundary-only RBF collocation schemes, which employs both the semi-analytical basis functions to approximate the FDE solutions. Inspired by the boundary collocation RBF techniques, the Method of Approximate Particular Solutions is one of the domain-type RBF collocation schemes with easy-to-use merit, which employs the particular solution RBFs for the solution of FDEs. This study will make a numerical investigation on the abovementioned RBF meshless methods to fractional derivative problems. The convergence rate, numerical accuracy and stability of these schemes will be examined through several benchmark examples.
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Groeger, Philip, Ulrich Denker, Robin Zech, Stefan Buhl, Matthias Ruhm, Mingyu Kim, Hongseok Jang, et al. "Advanced CD uniformity correction using radial basis function (RBF) models." In Metrology, Inspection, and Process Control XXXVI, edited by John C. Robinson and Matthew J. Sendelbach. SPIE, 2022. http://dx.doi.org/10.1117/12.2607571.

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Yassin, Ihsan Mohd, Mohd Nasir Taib, Mohd Zafran Abdul Aziz, Norasmadi Abdul Rahim, Nooritawati Md Tahir, and Aiman Johari. "Identification of DC motor drive system model using Radial Basis Function (RBF) Neural Network." In 2011 IEEE Symposium on Industrial Electronics and Applications (ISIEA 2011). IEEE, 2011. http://dx.doi.org/10.1109/isiea.2011.6108685.

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Wang, Lyu, Yuan Yun, Bin Zhang, and Tao Zhang. "Multi-Disciplinary Optimization of Space Experimental Device Using Radial Basis Function in ModelCenter." In ASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/detc2018-85168.

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The multi-disciplinary optimization of actuators which is the key component of a Nested Flying Vehicle (NFV) is done through ModelCenter. The NFV is operated inside the space station for space science experiments. The conceptual design of the NFV has been done with expensive simulation models. However, the optimization result of the NFV using the genetic algorithm led to an unaccepted time consumption. In order to solve those problems, a self-programmed optimizer which is embedded the Radial Basis Function (RBF)-based optimization strategy is developed and integrated to ModelCenter and applied to the electromagnetic actuators of NFV. The optimizer helps to integrate more metamodel-based optimization algorithms and enhance the optimization ability of ModelCenter. By comparing with the optimization algorithms supplied by ModelCenter, the RBF-based optimization strategy proposed in this paper costs less time and results in an acceptable design. The conceptual design of the NFV, the methodology of RBF-based optimization strategy and the key components of optimizer are introduced in this paper.
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Gan, Chengyu, Kourosh Danai, and Giorgio Rizzoni. "Fault Diagnosis of an Internal Combustion Engine With Embedded Radial Basis Function Modules." In ASME 2000 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2000. http://dx.doi.org/10.1115/imece2000-2328.

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Abstract The application of a modeling compensation method is studied in fault diagnosis. This compensation method relies on embedding single-input single-output radial basis function (RBF) modules within the nonlinear model of the plant so as to provide a basis for closed-form representation of the uncertain components. The weights of these RBF modules are then adapted on-line by the extended Kalman filter (EKF), in tandem with state estimation, to provide a representation of these uncertain components within their range of variation. The improved performance of the EKF-based observer in presence of time-variability has motivated exploring its utility in fault diagnosis of an automotive engine, where its performance is studied in detection of a throttle sensor fault.
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Wang, Rongqiao, Jianxing Mao, and Dianyin Hu. "Research on Surrogate Model Based on Local Radial Point Interpolation Method." In ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/detc2015-46689.

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In order to increase the accuracy of surrogate models in structural reliability analysis, we put forward a kind of surrogate model based on local radial point interpolation method (LRPIM). Three kinds of radial basis function (RBF) are employed for the shape function construction to form different kinds of LRPIM model. In order to illustrate the approximating ability of each surrogate model, we build up a nonlinear function model and carry out a numerical experiment on gas turbine disk’s estimated life-span. Compared with polynomial model, Chebyshev orthogonal polynomial model, Kriging model and RBF neural network model, LRPIM model has a demonstrable difference in terms of accuracy. For different polynomial basis order with constant sampling nodes amount, we conclude that fluctuant accuracy can be described by the balance between the describing improvement brought by polynomial basis order increase and the local impairment brought by support domain expansion. For sampling nodes amount with constant polynomial basis order, we conclude that accuracy of LRPIM model improves when sampling nodes amount increases. In order to illustrate the potential in reliability analysis, we apply the best performing LRPIM model to a set of widely used test problems, which certifies the accuracy and robustness of this kind of surrogate model. In a word, LRPIM model is one of the most promising surrogate models compared with other models on nonlinear approximating problems and reliability analysis.
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Zhang, Jie, Souma Chowdhury, Achille Messac, Luciano Castillo, and Jose Lebron. "Response Surface Based Cost Model for Onshore Wind Farms Using Extended Radial Basis Functions." In ASME 2010 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/detc2010-29121.

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This paper develops a cost model for onshore wind farms in the U.S.. This model is then used to analyze the influence of different designs and economic parameters on the cost of a wind farm. A response surface based cost model is developed using Extended Radial Basis Functions (E-RBF). The E-RBF approach, a combination of radial and non-radial basis functions, can provide the designer with significant flexibility and freedom in the metamodeling process. The E-RBF based cost model is composed of three parts that can estimate (i) the installation cost, (ii) the annual Operation and Maintenance (O&M) cost, and (iii) the total annual cost of a wind farm. The input parameters for the E-RBF based cost model include the rotor diameter of a wind turbine, the number of wind turbines in a wind farm, the construction labor cost, the management labor cost and the technician labor cost. The accuracy of the model is favorably explored through comparison with pertinent real world data. It is found that the cost of a wind farm is appreciably sensitive to the rotor diameter and the number of wind turbines for a given desirable total power output.
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Li, Zhigang, Jingwen Hu, and Jinhuan Zhang. "Comparison of Different Radial Basis Functions in Developing Subject-Specific Infant Head Finite Element Models for Injury Biomechanics Study." In ASME 2012 Summer Bioengineering Conference. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/sbc2012-80162.

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Developing a subject-specific finite element (FE) model, especially with only high quality hexahedral solid elements and quadrilateral shell elements, is very time-consuming. Recently, template-based mesh morphing method has become popular to construct subject-specific FE models, in which a baseline FE mesh can be morphed into a FE model with subject-specific geometry. Because the mesh morphing algorithm could be programmed and run automatically, it is a very promising method for future applications of subject-specific FE models in injury biomechanics studies. Radial Basis Function (RBF) as a powerful spatial interpolation method has already been used as a mesh morphing method (1). The types of RBFs can affect the morphed mesh quality and geometry accuracy in the RBF method. However, to date, no previous study has tried to compare the differences generated by different RBFs. Therefore, in this study, different RBFs were used to morph a baseline infant head FE model into 10 different subject-specific infant head FE models based on CT images from 10 children aged from 0 to 3 months. The mesh quality and geometry accuracy of the subject-specific models generated by different RBFs were compared using statistic analysis.
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Mullur, Anoop A., and Achille Messac. "Extended Radial Basis Functions for Metamodeling: A Comparative Study." In ASME 2005 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2005. http://dx.doi.org/10.1115/detc2005-85041.

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The process of constructing computationally benign approximations of expensive computer simulation codes, or metamodeling, is a critical component of several large-scale Multidisciplinary Design Optimization approaches. Such applications typically involve complex models, such as finite elements, computational fluid dynamics, or chemical processes. The decision regarding the most appropriate metamodeling approach usually depends on the type of application. However, several newly-proposed kernel-based metamodeling approaches can provide consistently accurate performance for a wide variety of applications. The authors recently proposed one such novel and effective metamodeling approach — the Extended Radial Basis Function approach — and reported encouraging results. To further understand the advantages and limitations of this new approach, we compare its performance to that of the typical radial basis function approach, and another closely related method — kriging. Several test functions with varying problem dimensions and degrees of nonlinearity are used to compare the accuracies of the metamodels using these metamodeling approaches. We consider several performance criteria, such as metamodel accuracy. effect of sampling technique, effect of problem dimension, and computational complexity. The results suggest that the E-RBF approach is a potentially powerful metamodeling approach for MDO-based applications.
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GAO, MINGLIANG, SHAN GAO, CHUANG YU, DEQUAN LI, SHIJI SONG, HAIMING SHI, HONGLIANG SUN, and HONGCHAO WANG. "RESEARCH AND APPLICATION OF RADIAL BASIS NETWORK BOGIE FAULT DIAGNOSIS MODEL BASED ON PARTICLE SWARM OPTIMIZATION." In 3rd International Workshop on Structural Health Monitoring for Railway System (IWSHM-RS 2021). Destech Publications, Inc., 2021. http://dx.doi.org/10.12783/iwshm-rs2021/36030.

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Bogie system is the key system that affects the safety and quality of EMU operation. The construction of fault diagnosis model for bogie system can effectively improve the safety and comfort of EMU operation. The traditional modeling method uses BP neural network to model by fitting bogie system temperature and other parameters. However, BP neural network is prone to fall into local minimum, slow convergence and poor diagnostic accuracy. In this paper, particle radial basis function neural network (PSRB) is designed by using particle swarm optimization algorithm with high convergence. Particle Swarm optimization (PSO) is used to optimize the parameters of RBF Neural Networks. According to the complexity of the input parameters of the bogie system, the input and output parameters of the model are determined. Particle swarm optimization algorithm is used to search the optimal values of the center, width and output layer weight threshold of the RBF neural network. The hybrid algorithm is applied to the fault diagnosis of bogie system, and a bogie fault diagnosis model based on particle radial basis function neural network is designed. The experimental results show that the diagnosis model can effectively improve the identification accuracy of fault diagnosis, the minimum error accuracy is 0.0055, the operation time is saved, the operation time is reduced to 1.9s, and the influence of non-target parameters on the inversion results is eliminated. The model can also be used in other EMU systems, and has practical application value.
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