Risk-sensitive optimal control for stochastic recurrent neural networks

Ziqian Liu, R. E. Torres, Miltiadis Kotinis
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引用次数: 1

Abstract

As a continuation of our study, this paper extends our research results of optimality-oriented control from deterministic recurrent neural networks to stochastic recurrent neural networks, and presents a new theoretical design for the risk-sensitive optimal control of stochastic recurrent neural networks. The design procedure follows the technique of inverse optimality, and obtains risk-sensitive state feedback controllers that guarantee an achievable meaningful cost for a given risk-sensitivity parameter.
随机递归神经网络的风险敏感最优控制
作为研究的延续,本文将面向最优控制的研究成果从确定性递归神经网络扩展到随机递归神经网络,提出了随机递归神经网络风险敏感最优控制的新理论设计。设计过程采用逆最优性技术,得到风险敏感状态反馈控制器,保证给定的风险敏感参数具有可实现的有意义的成本。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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