by N Sundararajan (Nanyang Technological University, Singapore) , P Saratchandran (Nanyang Technological University, Singapore) , & Lu Ying Wei (Kent Ridge Digital Laboratories, Singapore)

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.

• A Review of Radial Basis Function (RBF) Neural Networks
• A Novel Sequential Learning Algorithm for Minimal Resource Allocation Neural Networks (MRAN)
• MRAN for Function Approximation and Pattern Classification Problems
• MRAN for Nonlinear Dynamic Systems
• MRAN for Communication Channel Equalization