Neuro-evolutionary based controller design for linear and non-linear systems

Samarth Singh, K. Kishore, S. A. Akbar
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引用次数: 2

Abstract

In the present work a Neuro-Evolution based approach has been used to train a neural network for control of some sample systems. This method makes use of Genetic algorithm, here it is generating a population of neural networks and introduces mutation for producing better off-springs for the next generation. The approach is kind of black box optimization and do not require any back propagation for training. It makes use of fitness function to evaluate performance of off-springs, this fitness function is based on a novel reward function which allows for quick and smooth settling of the sample system towards set point. In order to address dynamics of the system's time sequenced error has been taken as exogenous input for the neural network. The method has been tested on a linear first order system and a system having non linearity.
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基于神经进化的线性和非线性系统控制器设计
在本工作中,基于神经进化的方法被用于训练神经网络来控制一些样本系统。这种方法利用遗传算法,在这里它产生一个神经网络群体,并引入突变,为下一代产生更好的后代。这种方法是一种黑盒优化,不需要任何反向传播来进行训练。它利用适应度函数来评价后代的性能,该适应度函数基于一种新颖的奖励函数,允许样本系统快速平稳地向设定点沉降。为了解决系统的动力学问题,将时间序列误差作为神经网络的外源输入。该方法已在线性一阶系统和非线性系统上进行了试验。
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