An autonomous system with inner modeled associative memory

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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