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.

在以下内容中，我们已经在非阶段的多层次教师环境中支撑了HSLATIT的新学习算法。 DGPA算法在层次结构学习自动机模型中使用。 .3）我们在非组织的多站点切换环境中进行了大量算法，其算法的功效具有通用的dgori thm和广义SE__ <ri>几乎所有计算机结果都证实了Ourgorithm.4的有效性。4） IZE的两种EA，即Moea。 DSS）用于使用神经网络进行传统的技术分析。 225已经确认了ur方法的有效性。