A NOVEL ADAPTIVE CONTROL VIA SIMPLE RULE(S) USING CHAOTIC DYNAMICS IN A RECURRENT NEURAL NETWORK MODEL AND ITS HARDWARE IMPLEMENTATION

摘要

A novel idea of adaptive control via simple rule(s) using chaotic dynamics in a recurrent neural network model is proposed. Since chaos in brain was discovered, an important question, what is the functional role of chaos in brain, has been arising. Standing on a functional viewpoint of chaos, the authors have been proposing that chaos has complex functional potentialities and have been showing computer experiments to solve many kinds of "ill-posed problems", such as memory search and so on. The key idea is to harness the onset of complex nonlinear dynamics in dynamical systems. More specifically, attractor dynamics and chaotic dynamics in a recurrent neural network model are introduced via changing a system parameter, "connectivity", and adaptive switching between attractor regime and chaotic regime depending surrounding situations is applied to realizing complex functions via simple rule(s). In this report, we will show (1)Global outline of our idea, (2)Several computer experiments to solve 2-dimensional maze by an autonomous robot having a neural network, where the robot can recognize only rough directions of target with uncertainty and the robot has no pre-knowledge about the configuration of obstacles (ill-posed setting), (3) Hardware implementations of the computer experiments using two-wheel or two-legs robots driven by our neuro chaos simulator. Successful results are shown not only in computer experiments but also in practical experiments, (4) Making pseudo-neuron device using semiconductor and opto-electronic technologies, where the device is called "dynamic self-electro optical effect devices (DSEED)". They could be "neuromorphic devices" or even "brainmorphic devices".