基于遗传模糊神经网络算法的棉花轧花过程智能监控方法研究

    Quality Intelligent Monitor Controlling of Cotton Gin Process Based on Genetic Fuzzy Neural Network Algorithms

    • 摘要: 针对棉花轧花过程质量控制,这类缺乏精确数学描述的复杂实际对象,提出了基于遗传模糊神经网络的智能监控新方法,描述了遗传神经网络BP算法的工作原理,阐明了基于人的模糊经验规则实现的轧花过程控制的模型和学习算法。所提出的遗传算法同模糊神经网络相结合的方法提高了轧花过程监控的实时性,并具有预报性好的特点。

       

      Abstract: This method combines fuzzy BP networks with genetic algorithms. As system exists fuzzy variables, it can be used for fuzzy BP networks and there is a characteristic for genetic algorithms that is good to constringency. With genetic fuzzy neural network algorithms, a cotton gin process system, which is with quality intelligent monitor controlling in time was developed. It is an effective means on the occasions of lack of precise mathematical description and in the case of nonlinear characteristics. The proposed methods rise effectiveness of real time for monitor control of gin process. The principle on genetic fuzzy BP network was given and the model and leaning algorithm derived for gin process were described.

       

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