Editorial Artificial Neural Networks To Systems, Man, And Cybernetics: Characteristics, Structures, And Applications
Mohammad S. Obaidat · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · 1998
The goal of this special issue is to present recent high- quality papers that deal with the applications of artificial neural networks (ANN's) to systems, man, and cybernetics (SMC). This special issue explores the state-of-the art in the applications of ANN to the SMC community. ANN's technology has reached a degree of maturity as evidenced by the increasing number of applications including the ones that have been reduced to practice. In this editorial, we present background and theoretical information related to ANN's, general characteristics, models, applications, and structures of ANN's. RTIFICIAL neural networks (ANN's) and neural en- gineering/computing in the wide sense are among to- day's most rapidly developing scientific disciplines. ANN's are parallel computational models that consist mainly of in- terconnected adaptive processing units. These networks are considered fine-grained parallel implementation of nonlinear dynamic and static systems. An ANN is an abstract simulation of real nervous system that contains a collection of processing units or processing elements (PE's) communicating with each other via axon connections. Such a model resembles the axons and dendrites of the nervous system. Because of its self- organizing and adaptive nature, the model provides a new parallel and distributed paradigm that has the potential to be more robust and user-friendly than traditional schemes (1)-(14). The study of artificial neural networks is an attempt to simulate and understand biological processes in an intrigu- ing manner. Today, we are witnessing the dawn of a new revolution in technology that will revamp the infrastructure of many approaches to solve cybernetics, information, and system engineering problems, among others. It is of interest to define alternative computational paradigms that attempt to mimic the brain's operation in several ways. Neural networks are an alternative approach to the traditional von Neumann programming schemes.