Self-Constructing Systems and their Applications to System Identification and Control

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Self-Constructing systems construct and after themselves based on observed date. For systems describing nonnumerical phenomena, a procedure was developed that automatically constructs general rule-set models and detects changes in the data, where bilateral flows of information, generalization from the data to concepts and specialization of concepts that contradict to the data, is appropriately controlled. Also, self-constructing methods were developed for two types of neural networks : neural networks that can express both instances and concepts in a uniform manner, and those which can express concepts explicitly with their input gates. Moreover, a structure design method was proposed for neural networks which can easily cope with changes in the data, and a new optimization method was devised that can be employed in neural networks training and leads to acquiring creatively new functions.As a general framework for self-constructing systems, two network-type frameworks were proposed : Universal Learning Networks for representing continuous systems whose behaviors are described by real-valued variables, and Automaton Learning Network for discrete event systems where the variables take discrete values. Both of them have self-constructing and self-altering capability by learning. And efficient learning algorithms were devised. Also, an automatic structure determination method for the networks was developed.One of the major aims of self-constructing systems is to develop control systems that exhibit, by self-alteration, good performance despite of changes in their surrounding situations. Therefore, in the framework of Universal Learning Network, with control of a crane as an example, robust control systems were developed which perform satisfactorily well coping with changes in payload, initial and target positions of the load and external disturbances.