Pattern Recognition and Machine Learning

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