自构建小波神经网络的内模控制研究与应用

Research and Application of Internal Model Control Based on Self-built Wavelet Neural Network

考虑到小波神经网络隐含层神经元的数目决定了整个网络的规模和性能,根据小波基函数的激励强度和衰减程度可以添加或者删除小波神经网络隐含层神经元,优化了小波神经网络隐含层结构,采用自构建小波神经网络辨识内模控制系统的正模型和逆模型,该模型的神经网络结构可根据性能要求动态调整,从而改进了神经网络内模控制技术,实验结果表明,提出的控制方法比传统方法在鲁棒性和抗扰性方面具有更好的性能表现,各项指标均优于传统控制方法.实现氧化铝熟料烧结工艺优化。

Considering neurons number in the hidden layer of wavelet neural network determines the size and performance of the entire network, this paper use the excitation intensity and attenuation degree of wavelet function to add or delete the hidden layer neurons of wavelet neural network. Thus the structure of wavelet neural network hidden layer is optimized, the forward model and inverse model of internal model control system is identified by the self-built wavelet neural network, whose neural network structure can be dynamically adjusted based on performance requirements. Experimental results show that the proposed control method has better performance than traditional control methods in aspects of robustness and immunity because each algorithm index is better than traditional control method. Hence optimization of clinker sintering process is achieved.