An autonomous system with inner modeled associative memory

为了实现具有内模联想记忆的自主系统，研究人类大脑的功能和生物的学习方法是非常有益的。在此考虑下，我们主要针对强化学习开发了一种基于非对称概率密度函数的快速学习方法，以有效实现强化学习的试错。一种引入时变参数思想的学习方法，以适应快速变化的动态环境iii)具有函数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，比传统的存储模式更容易检索iv)时间序列数据的检索方法；特别是对于首数据相同后数据不同的多重时间序列数据，采用鲁棒混沌控制方法，引入混沌边缘的思想，使系统处于混沌状态与非混沌状态之间；引入随机上升梯度强化学习方法作为预测方法，混沌时间序列预测精度高；仿真研究表明，这些方法的有效性得到了验证

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