Radial Basis Function Networks 2

Radial Basis Function Networks 2

Chapter 1 An Overview of Radial Basis Function Networks J. Ghosh and A. Nag This chapter presents a broad overview of Radial Basis Function Net- works ( RBFNs ) , and facilitates an understanding of their properties by using concepts ...

Author: Robert J. Howlett

Publisher: Springer Science & Business Media

ISBN: 3790813680

Category: Computers

Page: 392

View: 727

The Radial Basis Function (RBF) neural network has gained in popularity over recent years because of its rapid training and its desirable properties in classification and functional approximation applications. RBF network research has focused on enhanced training algorithms and variations on the basic architecture to improve the performance of the network. In addition, the RBF network is proving to be a valuable tool in a diverse range of application areas, for example, robotics, biomedical engineering, and the financial sector. The two volumes provide a comprehensive survey of the latest developments in this area. Volume 2 contains a wide range of applications in the laboratory and case studies describing current industrial use. Both volumes will prove extremely useful to practitioners in the field, engineers, reserachers, students and technically accomplished managers.
Categories: Computers

Radial Basis Function Networks 1

Radial Basis Function Networks 1

We also tested a general architecture as in Figure 2 , by including gamma filters at the input layer of an RBF network ( the structure is sometimes called focused ) with gamma synapses , and the results are presented in Figure 4a .

Author: Robert J.Howlett

Publisher: Springer Science & Business Media

ISBN: 3790813672

Category: Computers

Page: 344

View: 937

The Radial Basis Function (RBF) neural network has gained in popularity over recent years because of its rapid training and its desirable properties in classification and functional approximation applications. RBF network research has focused on enhanced training algorithms and variations on the basic architecture to improve the performance of the network. In addition, the RBF network is proving to be a valuable tool in a diverse range of application areas, for example, robotics, biomedical engineering, and the financial sector. The two volumes provide a comprehensive survey of the latest developments in this area. Volume 1 covers advances in training algorithms, variations on the architecture and function of the basis neurons, and hybrid paradigms, for example RBF learning using genetic algorithms. Both volumes will prove extremely useful to practitioners in the field, engineers, researchers and technically accomplished managers.
Categories: Computers

Fully Tuned Radial Basis Function Neural Networks for Flight Control

Fully Tuned Radial Basis Function Neural Networks for Flight Control

The network candidates used in this study are, * Type-1: Growing RBF network (GRBFN) with only tuning the weights. * Type-2, GRBF network with all the parameters being adaptable. * Type-3: Growing and Pruning RBF Network (GAP RBFN) with ...

Author: N. Sundararajan

Publisher: Springer Science & Business Media

ISBN: 9781475752861

Category: Science

Page: 158

View: 737

Fully Tuned Radial Basis Function Neural Networks for Flight Control presents the use of the Radial Basis Function (RBF) neural networks for adaptive control of nonlinear systems with emphasis on flight control applications. A Lyapunov synthesis approach is used to derive the tuning rules for the RBF controller parameters in order to guarantee the stability of the closed loop system. Unlike previous methods that tune only the weights of the RBF network, this book presents the derivation of the tuning law for tuning the centers, widths, and weights of the RBF network, and compares the results with existing algorithms. It also includes a detailed review of system identification, including indirect and direct adaptive control of nonlinear systems using neural networks. Fully Tuned Radial Basis Function Neural Networks for Flight Control is an excellent resource for professionals using neural adaptive controllers for flight control applications.
Categories: Science

Radial Basis Function Neural Networks with Sequential Learning

Radial Basis Function Neural Networks with Sequential Learning

The conditional density function p1 ( y ( k ) ) is the sum of the fol- lowing components : - aexp ( − || y ( k ) – ŷ ; † || 2 / 202 ) , ŷ € Ym ‚ † ( 5.33 ) that is , - aexp ( − || y ( k ) – u ; – s † || 2 / 202 ) , u ; € Ums ‡ € Sm‚r ...

Author: N. Sundararajan

Publisher: World Scientific

ISBN: 9810237715

Category: Science

Page: 236

View: 283

A review of radial basis founction (RBF) neural networks. A novel sequential learning algorithm for minimal resource allocation neural networks (MRAN). MRAN for function approximation & pattern classification problems; MRAN for nonlinear dynamic systems; MRAN for communication channel equalization; Concluding remarks; A outline source code for MRAN in MATLAB; Bibliography; Index.
Categories: Science

Neural Networks and Statistical Learning

Neural Networks and Statistical Learning

4. Benaim, M. (1994). On functional approximation with normalized Gaussian units. Neural Computation, 6(2), 319–333. 5. Berthold, M. R., & Diamond, J. (1995). Boosting the performance of RBF networks with dynamic decay adjustment.

Author: Ke-Lin Du

Publisher: Springer Nature

ISBN: 9781447174523

Category: Mathematics

Page: 988

View: 682

This book provides a broad yet detailed introduction to neural networks and machine learning in a statistical framework. A single, comprehensive resource for study and further research, it explores the major popular neural network models and statistical learning approaches with examples and exercises and allows readers to gain a practical working understanding of the content. This updated new edition presents recently published results and includes six new chapters that correspond to the recent advances in computational learning theory, sparse coding, deep learning, big data and cloud computing. Each chapter features state-of-the-art descriptions and significant research findings. The topics covered include: • multilayer perceptron; • the Hopfield network; • associative memory models;• clustering models and algorithms; • t he radial basis function network; • recurrent neural networks; • nonnegative matrix factorization; • independent component analysis; •probabilistic and Bayesian networks; and • fuzzy sets and logic. Focusing on the prominent accomplishments and their practical aspects, this book provides academic and technical staff, as well as graduate students and researchers with a solid foundation and comprehensive reference on the fields of neural networks, pattern recognition, signal processing, and machine learning.
Categories: Mathematics

Advances in Neural Networks ISNN 2010

Advances in Neural Networks    ISNN 2010

2, the integration of RBF network and PSO algorithm is described. In Sect. 3, simulation experiment through two functions is done and the results are presented. The conclusions are given in Sect. 4.

Author: Bao-Liang Lu

Publisher: Springer Science & Business Media

ISBN: 9783642132773

Category: Computers

Page: 787

View: 962

This book and its sister volume constitutes the proceedings of the 7th International Symposium on Neural Networks, ISNN 2010, held in Shanghai, China, June 6-9, 2010. The 170 revised full papers of Part I and Part II were carefully selected from 591 submissions and focus on topics such as Neurophysiological Foundation, Theory and Models, Learning and Inference, and Neurodynamics. The second volume, Part II (LNCS 6064) covers the following 5 topics: SVM and Kernel Methods, Vision and Image, Data Mining and Text Analysis, BCI and Brain Imaging, and applications.
Categories: Computers

Recent Advances in Radial Basis Function Collocation Methods

Recent Advances in Radial Basis Function Collocation Methods

205–242 2. S. Chen, C.F.N. Cowan, P.M. Grant, Orthogonal least-squares learning algorithm for radial basis function networks. IEEE Trans. Neural Netw. 2(2), 302–309 (1991) 3. C. Cortes, V. Vapnik, Support-vector networks. Mach. Learn.

Author: Wen Chen

Publisher: Springer Science & Business Media

ISBN: 9783642395727

Category: Technology & Engineering

Page: 90

View: 455

This book surveys the latest advances in radial basis function (RBF) meshless collocation methods which emphasis on recent novel kernel RBFs and new numerical schemes for solving partial differential equations. The RBF collocation methods are inherently free of integration and mesh, and avoid tedious mesh generation involved in standard finite element and boundary element methods. This book focuses primarily on the numerical algorithms, engineering applications, and highlights a large class of novel boundary-type RBF meshless collocation methods. These methods have shown a clear edge over the traditional numerical techniques especially for problems involving infinite domain, moving boundary, thin-walled structures, and inverse problems. Due to the rapid development in RBF meshless collocation methods, there is a need to summarize all these new materials so that they are available to scientists, engineers, and graduate students who are interest to apply these newly developed methods for solving real world’s problems. This book is intended to meet this need. Prof. Wen Chen and Dr. Zhuo-Jia Fu work at Hohai University. Prof. C.S. Chen works at the University of Southern Mississippi.
Categories: Technology & Engineering

Environmental and Hydrological Systems Modelling

Environmental and Hydrological Systems Modelling

Lin, G.-F. and Chen, L.-H. (2004): A non-linear rainfall-runoff model using radial basis function net- work. ... Neural Computations, 1(2), 281–294. ... Orr, M.J.L. (1996): Introduction to radial basis function networks.

Author: A W Jayawardena

Publisher: CRC Press

ISBN: 9780415465328

Category: Technology & Engineering

Page: 540

View: 902

Mathematical modelling has become an indispensable tool for engineers, scientists, planners, decision makers and many other professionals to make predictions of future scenarios as well as real impending events. As the modelling approach and the model to be used are problem specific, no single model or approach can be used to solve all problems, and there are constraints in each situation. Modellers therefore need to have a choice when confronted with constraints such as lack of sufficient data, resources, expertise and time. Environmental and Hydrological Systems Modelling provides the tools needed by presenting different approaches to modelling the water environment over a range of spatial and temporal scales. Their applications are shown with a series of case studies, taken mainly from the Asia-Pacific Region. Coverage includes: Population dynamics Reaction kinetics Water quality systems Longitudinal dispersion Time series analysis and forecasting Artificial neural networks Fractals and chaos Dynamical systems Support vector machines Fuzzy logic systems Genetic algorithms and genetic programming This book will be of great value to advanced students, professionals, academics and researchers working in the water environment.
Categories: Technology & Engineering

Neural Networks and Soft Computing

Neural Networks and Soft Computing

5, 1177–1 183 Rybowski R. (1998) Classification of incomplete feature vector by radial basis function networks, ... Neural Networks, 2, 2, 302–309 Billings S.A., Zheng G.L. (1995) Radial basis network configuration using genetic ...

Author: Leszek Rutkowski

Publisher: Springer Science & Business Media

ISBN: 9783790819021

Category: Computers

Page: 914

View: 516

This volume presents new trends and developments in soft computing techniques. Topics include: neural networks, fuzzy systems, evolutionary computation, knowledge discovery, rough sets, and hybrid methods. It also covers various applications of soft computing techniques in economics, mechanics, medicine, automatics and image processing. The book contains contributions from internationally recognized scientists, such as Zadeh, Bubnicki, Pawlak, Amari, Batyrshin, Hirota, Koczy, Kosinski, Novák, S.-Y. Lee, Pedrycz, Raudys, Setiono, Sincak, Strumillo, Takagi, Usui, Wilamowski and Zurada. An excellent overview of soft computing methods and their applications.
Categories: Computers

Machine Learning with Neural Networks

Machine Learning with Neural Networks

Hertz, Krogh, and Palmer [1] discuss radial basis-function networks with normalised radial basis functions u j exp ( − ∣ ∣ x − w j ∣ ∑ 12s2j ∣2 ) (x) ( m k=1 exp − 12s2k |x − wk|2 ) . (10.39) Other choices for radial basis ...

Author: Bernhard Mehlig

Publisher: Cambridge University Press

ISBN: 9781108849562

Category: Science

Page:

View: 919

This modern and self-contained book offers a clear and accessible introduction to the important topic of machine learning with neural networks. In addition to describing the mathematical principles of the topic, and its historical evolution, strong connections are drawn with underlying methods from statistical physics and current applications within science and engineering. Closely based around a well-established undergraduate course, this pedagogical text provides a solid understanding of the key aspects of modern machine learning with artificial neural networks, for students in physics, mathematics, and engineering. Numerous exercises expand and reinforce key concepts within the book and allow students to hone their programming skills. Frequent references to current research develop a detailed perspective on the state-of-the-art in machine learning research.
Categories: Science