Connectionist learning for control: an overview

THIS REPORT IS AN INTRODUCTORY OVERVIEW OF LEARNING BY CONNECTIONIST NETWORKS, ALSO CALLED ARTIFICIAL NEURAL NETWORKS, WITH A FOCUS ON THE IDEAS AND METHODS MOST RELEVANT TO THE CONTROL OF DYNAMICAL SYSTEMS. IT IS INTENDED BOTH TO PROVIDE AN OVERVIEW OF CONNECTIONIST IDEAS FOR CONTROL THEORISTS AND TO PROVIDE CONNECTIONIST RESEARCHERS WITH AN INTRODUCTION TO CERTAIN ISSUES IN CONTROL. THE PERSPECTIVE TAKEN EMPHASIZES THE CONTINUITY OF THE CURRENT CONNECTIONIST RESEARCH WITH MORE TRADITIONAL RESEARCH IN CONTROL, SIGNAL PROCESSING, AND PATTERN CLASSIFICATION. CONTROL THEORY IS A WELL-DEVELOPED FIELD WITH A LARGE LITERATURE, AND MANY OF THE LEARNING METHODS BEING DESCRIBED BY CONNECTIONISTS ARE CLOSELY RELATED TO METHODS THAT ALREADY HAVE BEEN INTENSIVELY STUDIED BY ADAPTIVE CONTROL THEORISTS. ON THE OTHER HAND, THE DIRECTIONS THAT CONNECTIONISTS ARE TAKING THESE METHODS HAVE CHARACTERISTICS THAT ARE ABSENT IN THE TRADITIONAL ENGINEERING APPROACHES. THIS REPORT DESCRIBES THESE CHARACTERISTICS AND DISCUSSES THEIR POSITIVE AND NEGATIVE ASPECTS. IT IS ARGUED THAT CONNECTIONISTS APPROACHES TO CONTROL ARE SPECIAL CASES OF MEMORY--INTENTIVE APPROACHES, PROVIDED A SUFFICIENTLY GENERALIZED VIEW OF MEMORY IS ADOPTED. BECAUSE ADAPTIVE CONNECTIONIST NETWORKS CAN COVER THE RANGE BETWEEN STRUCTURELESS LOOKUP TABLES AND HIGHLY CONSTRAINED MODEL-BASED PARAMETER ESTIMATION, THEY SEEM WELL-SUITED FOR THE ACQUISITION AND STORAGE OF CONTROL INFORMATION. AD