An autonomous system with inner modeled associative memory

具有内部建模联想记忆的自治系统

• 批准号: 13450176
• 负责人: OBAYASHI Masanao
• 金额: $ 4.16万
• 依托单位: Yamaguchi University
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Scientific Research (B)
• 财政年份: 2001
• 资助国家: 日本
• 起止时间: 2001 至 2003
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-13450176/
• 关键词: Learning; Chaotic Neural network; Reinforcement Learning; Nonlinear system control; Chaos; Edge of chaos; Associative memory; Robot; 階層型連想記憶; 非線形制御; 免疫システム; ニューラルネットワーク; 海馬連合野モデル; 時系列データ; 1対多; ロボット制御; カオスの辺縁; 関数型記憶行列; 情報生成; 自己組織化

To realize the autonomous system with inner modeled associative memory, it is very useful to research the function of the human brain and learning method of the living things. Under these considerations, we mainly developedi) for reinforcement learning, a faster learning method with asymmetric probability density function to realize trial and error effectivelyii) for reinforcement learning, a learning method introducing the idea of time-varying parameters in order to adapt to dynamical environments which change rapidlyiii) chaotic neural networks with function typed synaptic weights which enable easier retrieval of stored patterns than conventional in the case that each stored patterns have strong correlation each otheriv) a retrieval method for time-series data, particularly for plural time-series data which have same first data but different after thatv) robust chaotic control method introducing the idea of edge of chaos that the system is in the state between chaotic state and non-chaotic statevi) high precision of chaotic time series prediction by introducing the stochastic ascent gradient reinforcement learning method as prediction methodThese validity has been clarified by simulation studies

研究人脑的功能和生物的学习方法对于实现具有内部建模联想记忆的自主系统是非常有用的。基于这些考虑，我们主要开发了i）针对强化学习，一种具有非对称概率密度函数的更快学习方法，以有效地实现试错ii）针对强化学习，一种引入时变参数思想以适应动态环境的学习方法变化迅速iii) 具有函数类型突触权重的混沌神经网络，在每个存储模式彼此具有强相关性的情况下，能够比传统方式更容易地检索存储模式iv) 时间序列数据的检索方法，特别是多个时间序列数据其中有相同的v）引入混沌边缘思想的鲁棒混沌控制方法，使系统处于混沌状态和非混沌状态之间的状态vi）引入随机上升梯度强化学习方法，实现混沌时间序列预测的高精度作为预测方法这些有效性已通过模拟研究得到澄清

项目成果

K.Kobayashi: "A chaotic memory search model based on associative dynamics using features in stored patterns"Proc.of 41^<st> Society of Instrument and Control Engineering Annual Conference. 2919-2924 (2002)

K.Kobayashi：“使用存储模式中的特征，基于关联动力学的混沌记忆搜索模型”Proc.of 41^<st> 仪器与控制工程学会年会。

K.Umesako, M.Obayashi, K.Kobayashi: "Evolutionary and time-varying reinforcement learning system based on overlap of rules"Proc.of 6th Japan-France Congress on Mechatronics and 4^<th> Asia-Europe Congress on Mechatronics (JFM2003). 202-207 (2003)

K.Umesako，M.Obayashi，K.Kobayashi：“基于规则重叠的进化和时变强化学习系统”第6届日法机电一体化大会和第4届亚欧机电一体化大会的Proc.（

M.Obayashi: "Associative Memory and Mutual Information In a Chaotic Neural Network Introducing Function Typed Synaptic Weights"Transactions of Institute of Electrical Engineers of Japan. Vol.123, No.9. 1631-1637 (2003)

M.Obayashi：“引入函数类型突触权重的混沌神经网络中的联想记忆和互信息”日本电气工程师学会汇刊。

大林正直, 夕田憲治, 大宮理恵, 小林邦和: "開放型記憶行列を持つカオスニューラルネット連想記憶システムと相互情報量"電気学会論文誌C. 123・9. 1631-1637 (2003)

Masanao Obayashi、Kenji Yuta、Rie Omiya、Kunikazu Kobayashi：“具有开放记忆矩阵和互信息的混沌神经网络联想记忆系统”日本电气工程师学会会刊 C. 123・9 (2003)。

梅迫公輔, 大林正直, 小林邦和: "時変パラメータを持つ進化的強化学習システム"電気学会論文誌C. 124・7. (2004)

Kosuke Umesako、Masanao Obayashi、Kunikazu Kobayashi：“具有时变参数的进化强化学习系统”，日本电气工程师学会汇刊 C. 124, 7. (2004)