Dynamic recurrent wavelet network controllers for nonlinear system control
Cheng‐Jian Lin, Chi‐Yung Lee, Cheng‐Chung Chin · Journal of the Chinese Institute of Engineers · 2006
To solve nonlinear system control problems, a Recurrent Wavelet Network Controller (RWNC) is proposed in this paper. The proposed RWNC model has four‐layer structure. Temporal relations embedded in the network by adding some feedback connections representing the memory units in the second layer. A self‐organizing learning algorithm, which consists of structure learning and parameter learning, is proposed and is able to construct the recurrent wavelet network dynamically. The structure learning is based on the input partitions to determine the number of wavelet bases, and the parameter learning is based on the supervised gradient descent method to adjust the shape of wavelet functions, feedback weights, and the connection weights. Computer simulations were conducted to illustrate the performance and applicability of the proposed model.