This volume provides a comprehensive introduction to the use of neural networks in mechanical engineering applications. Beginning with an overview of different neural network topologies in the first two parts, functioning of human brain is also explained as an analogy with artificial models. Unsupervised models like Hopfield, Bi-directional Associative Memory, fuzzy Associative Memory, Adaptive Resonance Theory, kohonen as well as supervised architectures like Multi-Layer Perceptron, Counter Propagation networks and Radial Basis Function Networks are presented. The third part deals with applications of artificial neural networks for solving of design optimization problems, forward and inverse dynamic analysis applications and system identification and monitoring, as well as motion and vibration control in robotics and structural engineering. Software implementations for neural networks in C/C++ language and necessary optimization techniques in network training are given in Appendices. Key Features: · Latest developments in standard neural network architectures like Fuzzy ARTMAP · Network models such as Probabilistic and General Regression Neural Networks (GRNN) · Carefully designed simulation examples · Numerical Examples Contents: 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 back propagation /Training aspects and variations of back propagation method / Back propagation 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 back propagation 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

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