Adaptive-intelligent control by neural-net systems

Abstract

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.