As for precisely analyzing switching operations of the static induction (SI) thyristor with high speed and high power capability, this paper presents a numerical algorithm that employs the Newton-Raphson method in solving circuit equations implicitly containing a two-dimensional physical model. It is necessary for the algorithm to quickly and accurately compute the physical model whose hierarchy is lower than that of circuit equations. Therefore, Adler's irregular mesh structure is modified to integrate vector elements of conduction currents by using Scharfetter-Gummel's method. Displacement currents are counted in calculating terminal currents. It is made clear that displacement currents are indispensable to the current continuity condition within the SI thyristor.
The turn-off process of the basic SI thyristor with buried gates has been simulated by the numerical algorithm. The algorithm has been proved to be physically reasonable from the computed result that the locus of an input/output operating point has been involved in an input/output load surface in a three-dimensional space with current, voltage, and time coordinates. The power described in this paper is rather small in spite of the high power capability of the SI thyristor. We will simulate switching operations under more practical conditions by developing the numerical algorithm.

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The behavior of neural networks is often described in terms of competition and cooperation formation. Cooperativity in neural networks probably takes many forms. Some of them are surely represented in the mathematical models and computer simulations to which the cooperativity has been applied. This paper is an attempt to add another level of meaning to computational cooperativity by using an reinforcement learning network with generalized learning automata. The collection of learning automata in the team situation acts as self-interested agents that work toward improving their performance with respect to their individual preference ordering. In the global state space of the network, the case of partially synchronous stochastic process is considered. In this case, the existence of mean field is shown and a reinforcement learning algorithm which can make the dynamics on the average reinforcement trajectory is presented.

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With the progress of car electronics the saving of the wire number by use of information system such as LAN is said to be inevitable. In case of the introduction of LAN into car where an accident might be vitally related to human life, the system is requested to have higher processing ability against the crowdedness of signals.
In order to consider the conditions of such kind of LAN, we carried out a simulation study to find the dependence of the retardation characteristics on the modes of token control and on the status of informations.

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With the recent advancement of fast power switching devices, MOSFET, SIT and IGBT, a fast and simple method called instantaneous control has been often used for power conversion circuits. According to the method, detected information for load voltages and currents is processed with control circuits which consist mainly of analog operational and logic devices, and switching states in main circuits are determined with appropriate gate-signals fed back instantaneously in each operational case. There are interactions between main and control circuits and these two have to be solved simultaneously for circuit operational analysis. According to the conventional analysis method, there was a problem in equation formulation processes. But it is overcome by formulating main circuits with modified nodal equations (MNA) and control circuits with signal flow equations. Mixed circuits of two system types of elements can be analyzed. Further, equation formulation stamps, which are an advantage of the MNA, are deduced for main control elements. As an application example, an inverter system with the modified hysteresis control is analyzed for validation.

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The conventional sliding mode control or variable structure control (VSC) of nonlinear uncertain system is well known for its robust property and simplity of control law. However, the use of them is only pardonable on the assumption that the upper-bound of parameter variation or nonlinearity is known and that the complete information about state is available. Although the former has been solved with adaptive robust control theory recently, the latter seems not to be solved. In this paper, we tried to solve this problem using the technique of VSS adaptive robust control theory. That is, we proposed a VSS adaptive observer and a sliding mode control incorporated with this observer. We can prove the robust stability of the closed system applying the Lyapunov's second method.

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