As both the twenty-first century and the new millennium opened and the old eras passed into history, individuals and organizations throughout the world advanced their listings of the most significant people and events in their respective specialties. Possibly more important, the tum of the clock and calendar also offered these same observers a good reason to glance into the crystal ball. Presumably, the past is of greatest interest to most people when it permits better understanding of the present, and maybe even limited insight into the outlook. In keeping with the reflective mood of the...

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...

This excellent reference for all those involved in neural networks research and application presents, in a single text, the necessary aspects of parallel implementation for all major artificial neural network models. The book details implementations on varoius processor architectures (ring, torus, etc.) built on different hardware platforms, ranging from large general purpose parallel computers to custom built MIMD machines using transputers and DSPs.

Experts who performed the implementations author the chapters and research results are covered in each chapter. These results are...

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. Both theoretical analysis and experimental results for several bench mark problems are described. Guidelines for efficient parallel implementation are also highlighted. This book is suitable for graduate students, research scientists and practising engineers in the fields of artificial neural networks and parallel computing.

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...