Reinforcement of the Performances of the Soft Computing Techniques & Utilization of the learning Automaton-Challenges Toward Nonstationary Environment

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In the followings, we shall briefly touch upon our research results:1) We have proposed a new learning algorithm of the HSLA operating in the nonstationary multiteacher environment. We have shown that the proposed algorithm ensures convergence to the optimal set of actions with probability 1 under the general NME.2) We have generalized the DGPA algorithm to be used in the hierarchical structure learning automata model. We have shown that the generalized algorithm is ε optimal under the rather general condition. We have also presented several computer simulation results concerning the maze passing problem of the mobile robots that suggest the efficacy of the generalized algorithm.3) We have carried out large numbers of computer simulations in order to compare the efficacy of our algorithm in the nonstationary multiteacher switching environment with those of the generalized DGPA algorithm & the generalized SE_<RI> algorithm. Almost all of the computer simulation results confirm the effectiveness of our algorithm.4) We have tried to utilize two kinds of EAs, ie., the MOEA and the FEP for making the COMMONS GAME exciting. The MOEA has been used for generating various types of skilled players. Further the FEP has been introduced to find out appropriate combinations of the point of each card. We have succeeded in finding highly advanced rules compared with that of the original COMMONS GAME.5) We have proposed a new derision support system (DSS) for dealing stocks which improves the traditional technical analysis by using neural networks. In the proposed system, neural networks are utilized in order to predict the "Golden Cross" ('Dead Cross") several weeks it occurs. Computer simulation results concerning the dealings of the TOPIX and Nikkei-225 have confirmed the effectiveness of our approach.