Neural Networks

Preface / PART-1: OVERVIEW: Introduction / Brain as a neural network / Basic properties of neurons / Artificial neural networks / Conclusions / Exercises / Learning / Learning and training / Learning rules / Stability and plasticity / Conclusions / Exercises / PART-II: TYPES OF NEURAL NETWORKS / Hopfield Perceptron and related models / Hopfield model / Basic models of Hopfield network / Cellular neural networks / Perceptron / Other Associative models / Exercises / Appendix / Adaptive Resonance Theory / Network for ART-1 / ART-2 Network / Conclusions / Exercises / Self-Organization Maps / Kohonen Map / Adaptive or learning vector quantization / Multilayer self-organizing feature map / Exercises / Feed-forward back Propagation networks / Training of multilayer feed-forward networks by backpropagation /Training aspects and variations of backpropagation method / Backpropagation as stochastic approximation / Conclusions / Exercises / Hybrid learning neural networks / Counter Propagation network / Radial Basis Function Networks / Exercises / Probabilistic models, fuzzy ARTMAP and recurrent networks / Probabilistic neural networks / General Regression Neural networks / Fuzzy ARTMAP / Recurrent backpropagation neural networks / Exercises / PART-III: APPLICATIONS / Application of neural networks / Design and optimization of systems / System identification and Monitoring / Patterns Recognition Applications / Motion and vibration control applications / Summary / Exercises / Appendix-I Introduction of object oriented programming / Appendix-II Optimization schemes used in neural networks / Glossary / Index