For those entering the field of artificial neural networks, there has been an acute need for an authoritative textbook that explains the main ideas clearly and consistently using the basic tools of linear algebra, calculus, and simple probability th
Why do we feel a need to write a book about pattern recognition when many excellent books are already available on this classical topic? The answer lies in the depth of our coverage of neural networks as natural pattern classifiers and clusterers. A
Kalman Filtering and Neural Networks This self-contained book, consisting of seven chapters, is devoted to Kalman filter theory applied to the training and use of neural networks,and some applications of learning algorithms derived in this way.
Preface Chapter 1—Introduction 1.1 Neuroinformatics 1.1.1 Neural Memory: Neural Information Storage 1.1.2 Information-Traffic in the Neurocybernetic System 1.2 Information-Theoretic Framework of Neurocybernetics 1.3 Entropy, Thermodynamics and Infor
