Publisher's Synopsis
This text contains a comprehensive coverage of all the important network architectures that have been studied, together with recent theory. A broad range of the applications of each of the architectures is given. In addition, relevant concepts in pattern recognition, statistical theory and other mathematical prerequisites are included to aid the reader in understanding related network concepts.;Part 1 covers neural network basics, biological inspirations, a taxonomy of network types, and basic concepts in pattern recognition and statistics, while part 2 deals with early network architectures and applications and presents a brief review of basic concepts in linear algebra. There is a thorough description of the most popularly studied network, the multilayer feedforward network with back propagation training in part 3, and part 4 brings together recent findings in dynamic recurrent networks. Other important architectures including the RCE and neocognition frontiers of research are described in part 5 and unsupervised learning and related networks are introduced in part 6. The final part presents the theory and use of the little known, but important class of neural logic networks.
