A neural network which can identify a nonlinear system whose structure is very complex, is described. This neural network is constructed with four layers. In the second layer of the neural network, nonlinear relationship of input variables are generated and quantized accurately. So, high order effects of input variables can be considered in the neural network. The neural network is applied to a nonlinear system identification problem and the results are compared with those which are obtained by using another neural network and GMDH (Group Method of Data Handling) algorithm.

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We propose an algorithm for qualitative simulation of the large scaled physical systems such as plants. Such systems are often complicated for numerical simulation, thus the qualitative simulation is often used for analysis. We use the qualitative network model and implement a simulation algorithm modified for the qualitative network. This algorithm has three important futures : (1) devision of the propagation into two types, dynamic propagation and static propagations (2) balancing mechanism to avoid the undefined values, (3) rules for keeping consistencies on the network model.

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In this paper we present a new approach named the control configured design method. This method aims at designing a high performance machine system by combining a machine design and control, the concept of control configured vehicle (CCV). Performance index for this design method is defined in terms of ideal and actual inputs to the system. Applying the present method, we get feasible values of parameters for a car steering design.

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So far many studies about the production management systems have dealt with demand under uncertainty, which is regarded as a time-independent random variable.In this paper, we discuss the production management systems when demand follows Markov diffusions. The stochastic control method is applied to the analytical derivation of the optimal production policies. Main objects in this paper are to extend a deterministic model in the earlier contributions to the stochastic one and further to clear the properties of control in the production management systems, considering the differences of the shapes between some demand distributions.

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In this paper, we consider a collective model of learning automata operating on N-static random environments. Each automaton has a behavioural tactic directed towards the realization of its own goal, estimating the magnitude of the expected value of utility function that depends explicity on the automaton's strategy and the corresponding responses from environments. From game theoretic viewpoint, we can construct several types of environments for this model.
In here, a non-cooperative game on N-cooperative environments is proposed. For this model, the definitions, evaluation metrics and a distance diminishing reinforcement scheme are introduced. As an application, a simple numerical example is shown.

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[in Japanese]

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