Hybrid dynamic neural learning (HDNL) in control applications

Presents a new, simple, and versatile neural network controller paradigm, which applies a hybrid learning approach. The major advantage of this controller is that the network learning process is faster. The controller first applies a nonlinear neuron computation scheme to make functional association of input signals to an internal representation in frequency domain. It then maps the internal representation to desired patterns as the output of the controller. Unsupervised learning is conducted for tuning the synaptic weights from the input layer to the internal layer. Supervised learning is employed to tune the synaptic weights for output pattern matching. The hybrid neural controller is especially capable of handling highly noise-corrupted signals in many real-world control applications, such as real-time robot motion planning and control. The hybrid neural network controller was applied to a nonlinear process involving controlling the position of a bouncing ball over a rough moving surface. This system is a typical example of an uncertain model subjected to various types of noises. The simulation was done in a MATLAB environment.