P Saratchandran's Books

Parallel Implementations of Backpropagation Neural Networks on Transputers

A Study of Training Set Parallelism

By: P. Saratchandran,N. Sundararajan,Shou King Foo

Write a review Read reviews Take a Quiz Solve Book Puzzle

Parallel Implementations of Backpropagation Neural Networks on Transputers

A Study of Training Set Parallelism

By: P. Saratchandran,N. Sundararajan,Shou King Foo

This book presents a systematic approach to parallel implementation of feedforward neural networks on an array of transputers. The emphasis is on backpropagation learning and training set parallelism. Using systematic analysis, a theoretical model has been developed for the parallel implementation. The model is used to find the optimal mapping to minimize the training time for large backpropagation neural networks. The model has been validated experimentally on several well known benchmark problems. Use of genetic algorithms for optimizing the performance of the parallel implementations is described. Guidelines for efficient parallel implementations are highlighted.

Average ratings

N/A

Write a review

Solve jigsaw puzzle

Age

Page Count

228

Publisher

World Scientific

Published Date

ISBN 10

9810226543

ISBN 13

9789810226541

Radial Basis Function Neural Networks with Sequential Learning

MRAN and Its Applications

By: N. Sundararajan,P. Saratchandran,Ying Wei Lu

Write a review Read reviews Take a Quiz Solve Book Puzzle

Radial Basis Function Neural Networks with Sequential Learning

MRAN and Its Applications

By: N. Sundararajan,P. Saratchandran,Ying Wei Lu

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.

Average ratings

N/A

Write a review

Solve jigsaw puzzle

Age

Page Count

236

Publisher

World Scientific

Published Date

ISBN 10

9810237715

ISBN 13

9789810237714

Fully Tuned Radial Basis Function Neural Networks for Flight Control

By: N. Sundararajan,P. Saratchandran,Yan Li

Write a review Read reviews Take a Quiz Solve Book Puzzle

Fully Tuned Radial Basis Function Neural Networks for Flight Control

By: N. Sundararajan,P. Saratchandran,Yan Li

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.

Average ratings

N/A

Write a review

Solve jigsaw puzzle

Age

Page Count

167

Publisher

Springer Science & Business Media

Published Date

ISBN 10

1475752865

ISBN 13

9781475752861

Empirical Approach to Machine Learning

By: Plamen P. Angelov,Xiaowei Gu

Write a review Read reviews Take a Quiz Solve Book Puzzle

Empirical Approach to Machine Learning

By: Plamen P. Angelov,Xiaowei Gu

This book provides a ‘one-stop source’ for all readers who are interested in a new, empirical approach to machine learning that, unlike traditional methods, successfully addresses the demands of today’s data-driven world. After an introduction to the fundamentals, the book discusses in depth anomaly detection, data partitioning and clustering, as well as classification and predictors. It describes classifiers of zero and first order, and the new, highly efficient and transparent deep rule-based classifiers, particularly highlighting their applications to image processing. Local optimality and stability conditions for the methods presented are formally derived and stated, while the software is also provided as supplemental, open-source material. The book will greatly benefit postgraduate students, researchers and practitioners dealing with advanced data processing, applied mathematicians, software developers of agent-oriented systems, and developers of embedded and real-time systems. It can also be used as a textbook for postgraduate coursework; for this purpose, a standalone set of lecture notes and corresponding lab session notes are available on the same website as the code. Dimitar Filev, Henry Ford Technical Fellow, Ford Motor Company, USA, and Member of the National Academy of Engineering, USA: “The book Empirical Approach to Machine Learning opens new horizons to automated and efficient data processing.” Paul J. Werbos, Inventor of the back-propagation method, USA: “I owe great thanks to Professor Plamen Angelov for making this important material available to the community just as I see great practical needs for it, in the new area of making real sense of high-speed data from the brain.” Chin-Teng Lin, Distinguished Professor at University of Technology Sydney, Australia: “This new book will set up a milestone for the modern intelligent systems.” Edward Tunstel, President of IEEE Systems, Man, Cybernetics Society, USA: “Empirical Approach to Machine Learning provides an insightful and visionary boost of progress in the evolution of computational learning capabilities yielding interpretable and transparent implementations.”

Average ratings

N/A

Write a review

Solve jigsaw puzzle

Age

Page Count

437

Publisher

Springer

Published Date

ISBN 10

3030023842

ISBN 13

9783030023843

Minimum Error Entropy Classification

By: Joaquim P. Marques de Sá,Luís M.A. Silva,Jorge M.F. Santos,Luís A. Alexandre

Write a review Read reviews Take a Quiz Solve Book Puzzle

Minimum Error Entropy Classification

By: Joaquim P. Marques de Sá,Luís M.A. Silva,Jorge M.F. Santos,Luís A. Alexandre

This book explains the minimum error entropy (MEE) concept applied to data classification machines. Theoretical results on the inner workings of the MEE concept, in its application to solving a variety of classification problems, are presented in the wider realm of risk functionals. Researchers and practitioners also find in the book a detailed presentation of practical data classifiers using MEE. These include multi‐layer perceptrons, recurrent neural networks, complexvalued neural networks, modular neural networks, and decision trees. A clustering algorithm using a MEE‐like concept is also presented. Examples, tests, evaluation experiments and comparison with similar machines using classic approaches, complement the descriptions.

Average ratings

N/A

Write a review

Solve jigsaw puzzle

Age

Page Count

270

Publisher

Springer

Published Date

ISBN 10

3642290299

ISBN 13

9783642290299

Radial Basis Function Neural Networks With Sequential Learning, Progress In Neural Processing

By: Ying Wei Lu,P Saratchandran,Narasimman Sundararajan

Write a review Read reviews Take a Quiz Solve Book Puzzle

Radial Basis Function Neural Networks With Sequential Learning, Progress In Neural Processing

By: Ying Wei Lu,P Saratchandran,Narasimman Sundararajan

This book presents in detail the newly developed sequential learning algorithm for radial basis function neural networks, which realizes a minimal network. This algorithm, created by the authors, is referred to as Minimal Resource Allocation Networks (MRAN). The book describes the application of MRAN in different areas, including pattern recognition, time series prediction, system identification, control, communication and signal processing. Benchmark problems from these areas have been studied, and MRAN is compared with other algorithms. In order to make the book self-contained, a review of the existing theory of RBF networks and applications is given at the beginning.

Average ratings

N/A

Write a review

Solve jigsaw puzzle

Age

Page Count

231

Publisher

World Scientific

Published Date

ISBN 10

9814495271

ISBN 13

9789814495271

Parallel Implementations of Backpropagation Neural Networks on Transputers

A Study of Training Set Parallelism

By: P. Saratchandran,N. Sundararajan,Shou King Foo

Write a review Read reviews Take a Quiz Solve Book Puzzle

Parallel Implementations of Backpropagation Neural Networks on Transputers

A Study of Training Set Parallelism

By: P. Saratchandran,N. Sundararajan,Shou King Foo

Average ratings

N/A

Write a review

Solve jigsaw puzzle

Age

Page Count

228

Publisher

World Scientific

Published Date

ISBN 10

9810226543

ISBN 13

9789810226541

Book's by P Saratchandran

Parallel Implementations of Backpropagation Neural Networks on Transputers

Parallel Implementations of Backpropagation Neural Networks on Transputers

Radial Basis Function Neural Networks with Sequential Learning

Radial Basis Function Neural Networks with Sequential Learning

Fully Tuned Radial Basis Function Neural Networks for Flight Control

Fully Tuned Radial Basis Function Neural Networks for Flight Control

Empirical Approach to Machine Learning

Empirical Approach to Machine Learning

Minimum Error Entropy Classification

Minimum Error Entropy Classification

Radial Basis Function Neural Networks With Sequential Learning, Progress In Neural Processing

Radial Basis Function Neural Networks With Sequential Learning, Progress In Neural Processing

Parallel Implementations of Backpropagation Neural Networks on Transputers

Parallel Implementations of Backpropagation Neural Networks on Transputers

Username

Password

Username

Password

Password again

Birthday

Email