【摘要】 针对啤酒发酵过程中罐内温度控制问题,研究神经网络对模糊控制规则的优化方法,利用径向基函数神经网络对模糊控制规则进行优化,提高其自适应能力。以啤酒生产过程中主发酵阶段的数据作为输入样本,通过径向基函数神经网络进行学习训练,校正模糊控制规则,优化模糊控制器。优化前与优化后响应特性曲线的比较结果表明,RBF神经网络学习能力强,收敛速度快;模糊控制规则的完备性和一致性明显改善,控制器的响应速度快、超调量小、稳定性强、控制效果好。更多还原

【Abstract】 Aiming at the problem in the process of beer fermentation tank temperature control,the optimized method of fuzzy control rules based on the neural network was studied,fuzzy control rules were optimized by using the radial basis function neural network to improve its adaptive ability.In the beer production process,data of the main fermentation phase were taken as the input sample and trained by the radial basis function neural network,and fuzzy control rules were revised,the fuzzy controller was revised.Comparing the response characteristic curves before the optimization and that after,the results of the comparison show the RBF neural network has strong learning ability and fast convergence speed.The completeness and uniformity of fuzzy control rules are obviously improved,the controller has fast response speed,small overshoot,strong stability and good control effects.更多还原