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Adaptive–intelligent control by neural‐net systems
Author(s) -
Yamazaki Yoshitake,
Kang Geuntaek,
Ochiai Moyuru
Publication year - 1998
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/(sici)1098-111x(199806)13:6<503::aid-int6>3.0.co;2-p
Subject(s) - computer science , artificial neural network , intelligent control , set (abstract data type) , hierarchical control system , artificial intelligence , net (polyhedron) , adaptive control , control (management) , function (biology) , intelligent decision support system , boltzmann machine , restricted boltzmann machine , control system , mathematics , engineering , geometry , evolutionary biology , electrical engineering , biology , programming language
Adaptive–intelligent control by neural‐net systems is discussed. Actual adaptive–intelligent control is realized in a general system through the following two hierarchical steps: (1) choosing a hierarchical coordinate system (associated with the environment of the system) and constructing the hierarchical evaluation functions (specifying its control states) and (2) finding a set of the most appropriate hierarchical values for the control parameters (giving the minimum value to the evaluation function). Step 1 establishes “intelligently self‐controllable (thinking) algorithms” with human‐like intelligence for various events (concepts). Step 2 studies the intelligently self‐controllable (thinking) algorithms for finding the most appropriate state. Adaptive–intelligent control by neural‐net systems is realized by integrating both intelligently self‐controllable (thinking) algorithms on the neural‐net systems. Here step 2 is mainly discussed in the neural‐net systems of Boltzmann type machines using the method of stochastic dynamics. © 1998 John Wiley & Sons, Inc.