提出了一种基于小波神经网络整定的PID 控制方法。由于小波变换具有良

好的时频局部特性,神经网络具有强大的非线性映射能力,自学习、自适应等优势,采用规

范正交的小波函数作为神经网络的基函数构成小波神经网络,该网络兼有小波函数的紧

支性、波动性以及神经网络的非线性映射能力,自学习、自适应能力等优点,渗碳炉控制实

验结果表明,用该方法整定的PID 控制系统收敛速度快,逼近精度高,鲁棒性好-Based on wavelet neural network-tuning of PID control methods. Since the wavelet transform has good time-frequency localization properties, neural network has strong ability of nonlinear mapping, self-learning, adaptive and other advantages, the use of standardized orthogonal wavelet function as a neural network constitutes a wavelet basis function neural network, the network a combination of compactly supported wavelet function, and volatility as well as the neural network nonlinear mapping ability, self-learning, adaptive capacity, etc., carburizing furnace control experimental results show that using this method of tuning PID control system for fast convergence approximation of high accuracy, good robustness