火焰电容成像的模糊神经网络控制策略

APPLICATION OF NEURAL-FUZZY CONTROLLER IN FLAME CAPACITANCE TOMOGRAPHY

  • 摘要: 描述了基于电容层析成像,采用模糊逻辑和神经网络对静态火焰进行闭环控制过程.以神经元形式将专家知识和训练类型融合到模糊规则中,借助于神经网络的强大的学习能力,系统能自动地调整其性能.

     

    Abstract: This paper describes a capacitance tomography based closed loop control for stationary-flames using fuzzy logic and neural networks. Experts' knowledge and training patterns can be incorporated into fuzzy rules with neurons. With the help of powerful learning capability of neural networks,the system could adjust its performance efficiently.

     

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