Pattern Recognition and Machine Learning: Proceedings of the Japan—U.S. Seminar on the Learning Process in Control Systems, held in Nagoya, Japan August 18–20, 1970

This book contains the Proceedings of the US-Japan Seminar on Learning Process in Control Systems. The seminar, held in Nagoya, Japan, from August 18 to 20, 1970, was sponsored by the US-Japan Cooperative Science Program, jointly supported by the National Science Foundation and the Japan Society for the Promotion of Science. The full texts of all the presented papers except two t are included. The papers cover a great variety of topics related to learning processes and systems, ranging from pattern recognition to systems identification, from learning control to biological modelling. In order to reflect the actual content of the book, the present title was selected. All the twenty-eight papers are roughly divided into two parts--Pattern Recognition and System Identification and Learning Process and Learning Control. It is sometimes quite obvious that some papers can be classified into either part. The choice in these cases was strictly the editor's in order to keep a certain balance between the two parts. During the past decade there has been a considerable growth of interest in problems of pattern recognition and machine learn­ ing. In designing an optimal pattern recognition or control system, if all the a priori information about the process under study is known and can be described deterministically, the optimal system is usually designed by deterministic optimization techniques.

Table of Contents:
I: Pattern Recognition and System Identification.- Some Studies on Pattern Recognition with Nonsupervised Learning Procedures.- Linear and Nonlinear Stochastic Approximation Algorithms for Learning Systems.- Multi-Category Pattern Classification Using a Nonsupervised Learning Algorithm.- A Mixed-Type Non-Parametric Learning Machine Without a Teacher.- Recognition System for Handwritten Letters Simulating Visual Nervous System.- Sequential Identification by Means of Gradient Learning Algorithms.- Stochastic Approximation Algorithms for System Identification Using Normal Operating Data.- On Utilization of Structural Information to Improve Identification Accuracy.- An Inconsistency Between the Rate and the Accuracy of the Learning Method for System Identification and Its Tracking Characteristics.- Weighing Function Estimation in Distributed-Parameter Systems.- System Identification by a Nonlinear Filter.- A Linear Filter for Discrete Systems with Correlated Measurement Noise.- II: Learning Process and Learning Control.- Stochastic Learning by Means of Controlled Stochastic Processes.- Learning Processes in a Random Machine.- Learning Process in a Model of Associative Memory.- Adaptive Optimization in Learning Control.- Learning Control of Multimodal Systems by Fuzzy Automata.- On a Class of Performance-Adaptive Self-Organizing Control Systems.- A Control System Improving Its Control Dynamics by Learning.- Self-Learning Method for Time-Optimal Control.- Learning Control via Associative Retrieval and Inference.- Statistical Decision Method in Learning Control Systems.- A Continuous-Valued Learning Controller for the Global Optimization of Stochastic Control Systems.- On Variable-Structure Stochastic Automata.- A Critical Review of Learning Control Research.- Heuristics and Learning Control (Introduction to Intelligent Control).- Adaptive Model Control Applied to Real-Time Blood-Pressure Regulation.- Real-Time Display System of Response Characteristics of Manual Control Systems.- List of Discussors.