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

软计算技术性能的强化

基本信息

批准号：
18500173
负责人：
BABA Norio
金额：
$ 1.52万
依托单位：
Osaka Kyoiku University
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
2006
资助国家：
日本
起止时间：
2006 至 2007
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-18500173/
关键词：
Nonstationary Multiteacher Environment HSLA Mobile Robot Maze Passing DGPA & SERI COMMONS GAME Evolutionary Computation Prediction of Golden Cross and Dead Cross by Neura 学習オートマトンニューラルネット非定常環境融合技術知能工学ソフトコンピューテイング学習と適応

项目摘要

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.

下面，我们将简要介绍我们的研究成果：1)我们提出了一种新的运行在非平稳多教师环境下的HSLA学习算法。我们已经证明，在一般nme下，我们提出的算法保证以概率1收敛到最优动作集。2)我们已经推广了DGPA算法用于分层结构学习自动机模型。我们已经证明，在相当一般的条件下，广义算法是ε最优的。我们还给出了几个关于移动机器人迷宫通过问题的计算机模拟结果，这些结果表明了广义算法的有效性。3)我们进行了大量的计算机模拟，以比较我们的算法在非平稳多教师交换环境下与广义DGPA算法和广义SE_<RI>算法的有效性。几乎所有的计算机仿真结果都证实了算法的有效性。我们尝试使用了两种ea，即。， MOEA和FEP使COMMONS GAME变得令人兴奋。MOEA已被用于培养各种类型的熟练球员。此外，还引入了FEP来找出每个卡片点的适当组合。5)提出了一种新的股票交易决策支持系统（DSS），改进了传统的神经网络技术分析方法。在提出的系统中，利用神经网络来预测“金十字”（“死十字”）发生的几个星期。关于东证指数和日经225指数交易的计算机模拟结果证实了我们方法的有效性。