Abstract :
One important area in engineering cybernetics is that of learning systems—the analysis and synthesis of engineering systems which exhibit learning behavior. During the past decade there has been a considerable growth of research interest in problems of learning systems. Many different approaches have been proposed for the design of systems with learning capabilities. Engineering applications include pattern recognition, communication and antenna systems, and control and diagnostic problems. Recent progress has indicated that the research in learning systems has just gone through a transient period from discussions of general philosophy, concepts and formulations of the problem to studies of a possible unified theory. This progress is significant in the sense that, once a unified approach is successful, further research efforts can then be concentrated toward the depth of the theory and its applications. One of the major contributors in this direction is Prof. Tsypkin, the author of this book. Under a general approach of probabilistic iterative algorithms or stochastic approximation procedures, this book treats systematically a broad class of problems in learning systems. Many existing learning algorithms can be put under this same general formulation, and these are compared.
