A novel algorithm for wavelet neural networks with application to enhanced PID controller design

This paper presents a variable step-size updating algorithm for wavelet neural network (WNN) in setting the enhanced PID controller parameters. Compared to the iterative method with constant step-size, the most innovative character of the algorithm proposed is its capability of shortening tracking time and improving the convergence in weights updating process for complex systems or large-scale networks. By combining the relationship among WNN, the Kalman filter and the normalized least mean square (NLMS), we introduce the T–S fuzzy inference mechanism for activation derived functions. Furthermore, a once-through steam generator (OTSG) model is established for validating the practicability and reliability in a real complicated system. Finally, simulation results are presented to exhibit the effectiveness of the proposed variable step-size algorithm.