Nonlinear systems control using self-constructing wavelet networks
This paper describes a self-constructing wavelet network (SCWN) controller for nonlinear systems control. The proposed SCWN controller has a four-layer structure. We adopt the orthogonal wavelet functions as its node functions. An online learning algorithm, structure learning and parameter learning, allows the dynamic determining of the number of wavelet bases, and adjusting the shape of the wavelet bases and the connection weights. The SCWN controller is a highly autonomous system. Initially, there are no hidden nodes. They are created and begin to grow as learning proceeds. Computer simulations have been conducted to illustrate the performance and applicability of the proposed learning scheme.
online learning temperature control wavelet neural networks back-propagation degree measure