Fundamentals of Artificial Neural Networks

"In Fundamentals of Artificial Neural Networks, Mohamad Hassoun provides the first systematic account of artificial neural network paradigms by identifying clearly the fundamental concepts and major methodologies underlying most of the current theory and practice employed by neural network researchers. Such a systematic and unified treatment makes the subject more accessible to students and practitioners. Here, important results are integrated in order to more fully explain a wide range of existing empirical observations and commonly used heuristics. There are numerous illustrative examples, more than 200 end-of-chapter analytical and computer-based problems that will aid in the development of neural network analysis and design skills, and a bibliography of nearly 700 references. Proceeding in a clear and logical fashion, the book presents the basic building blocks and concepts of artificial neural networks, brings together supervised, reinforcement, and unsupervised learning rules in simple nets in a common framework, and then covers such topics as the convergence and solution properties of these learning rules, learning multilayer nets using backprop and its variants, major neural network paradigms, associative memories, energy minimizing nets, Boltzmann machines and Boltzmann learning, and other global search/optimization algorithms such as stochastic gradient search, simulated annealing, and genetic algorithms."--Page 4 of cover.