Learning & Adaptation of Soft Computing Techniques Operating in the Nonstationary Environment

学习

基本信息

批准号：
16500129
负责人：
BABA Norio
金额：
$ 0.77万
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：
2004
资助国家：
日本
起止时间：
2004 至 2005
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-16500129/
关键词：
Soft Computing Learning Automata Hierarchical Structure Nonstationary Environment Prediction of Stock Price Computer Gaming Fusion of Soft Computing Neural Networks 学習オートマン非定常環境学習適応学習オートマトン

项目摘要

Despite the current matured state concerning the theory and applications of the soft computing techniques, there are still several problems to be settled. One of the most important problems might be : "How to cope with the nonstationary environment ?" Recently, the following idea hit me : "Fusion of learning automata and soft computing techniques may contribute a lot in finding a successful way for solving the above problem."In this research project, I was mainly involved in the research to investigate the learning performance of the hierarchical structure learning automata under the unknown nonstationary multiteacher environment in order to check whether this idea is OK. I was also involved to the research which deals with the real problems that contain rather strong nonlinearity.The following are our research results :1.We proposed a new learning algorithm for the hierarchical structure learning automata operating in the nonstationary multiteacher environment. We proved that the proposed algorithm ensures convergence with probability 1 to the optimal path under a certain nonstationary multiteacher environment.2.In order to compare the learning performance of the proposed algorithm in the nonstationary multiteacher environment with the algorithms DGPA and SE_<RI> (two of the fastest algorithms today), we carried out a rather large numbers of the computer simulations. The simulation results we obtained confirm the effectiveness of the proposed algorithm.3.We have applied neural networks and genetic algorithms to the fields of financial engineering & computer gaming. We succeeded in obtaining the following results :(1)Neural networks are quite helpful in order to predict the Golden Cross and the Dead Cross several weeks before they occur.(2)Fusion of neural networks and genetic algorithms is also quite helpful for finding an appropriate rule to make the Environmental Game much more exciting.

尽管当前关于软计算技术的理论和应用的成熟状态，但仍有几个问题要解决。最重要的问题之一可能是：“如何应对非组织环境？”最近，以下想法打动了我：“学习自动机和软计算技术的融合可能会在找到解决上述问题的成功方法方面做出很大贡献。”在这项研究项目中，我主要参与研究，以研究学习绩效在未知的非组织多教学环境下学习自动机的分层结构，以检查这个想法是否还可以。我还参与了处理包含相当强大非线性的实际问题的研究。以下是我们的研究结果：1。我们提出了一种在非机构多教学环境中运行的层次结构学习自动机的新学习算法。我们证明了所提出的算法确保在某个非组织多教师环境下与概率1融合到最佳路径。当今的两种最快的算法），我们进行了大量的计算机模拟。我们获得的仿真结果确认了拟议算法的有效性。3。我们已将神经网络和遗传算法应用于金融工程和计算机游戏领域。我们成功地获得了以下结果：（1）神经网络在预测金十字和死亡十字架发生前几周非常有帮助。（2）神经网络和遗传算法的融合也有助于找到找到一个适当的规则使环境游戏更加令人兴奋。

项目成果

期刊论文数量（24）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Utilization of Neural Networks and Genetic Algorithms to Make Game Playing More Exciting

利用神经网络和遗传算法让游戏玩起来更刺激

DOI：
发表时间：
2005
期刊：
Gaming, Simulations, and Society (Shiratori et al., Editors)(Springer-Verlag)
影响因子：
0
作者：
Norio Baba;Wang Shuqin
通讯作者：
Wang Shuqin

A New Learning Algorithm for the Hierarchical Structure Learning Automata Operating in the General Multiteacher Environment

通用多教师环境下分层结构学习自动机的新学习算法

DOI：
发表时间：
2004
期刊：
IEICE Transactions on Information and Systems E87-D,5
影响因子：
0
作者：
今村晋一郎;河野英昭;前田博;生駒哲一;Norio Baba
通讯作者：
Norio Baba

Learning Behaviors of the Hierarchical Structure Learning Automata Operating in the General Nonstationary Multiteacher Environment

一般非平稳多教师环境下层次结构学习自动机的学习行为

DOI：
发表时间：
2004
期刊：
Proceedings of the 36^<th> ISCIE Int. Symp. Stochastic Systems Theory and Its Applications
影响因子：
0
作者：
Yasuda;S.;Nakamura;T.;Kawase;S.;et al.;S.Saito;Norio Baba
通讯作者：
Norio Baba

An Intelligent Utilization of Neural Networks for Improving the Traditional Technical Analysis in the Stock Markets

智能利用神经网络改进股票市场的传统技术分析