Kohonen neural network model reference for nonlinear discrete time systems

In this work, an adaptive neural network like Kohonen neural network (KNN) model reference is used for tracking control of nonlinear system. Proposed adaptive Kohonen neural network (ADKNN) are used to minimize the error between output and target signal for nonlinear discrete-time systems. The ADKNN is a feed-forward neural network help for approximation of the nonlinearities in the industrial plant and main characteristic of the system is taken into account is disturbances in the system. Tracking error by the adaptive ADKNN based approximation system is an important characteristic for the design and analysis. It is shown in results that the preference of the error system is decisive to the solution of tracking control. Difference between ADKNN output and reference signal can be made arbitrarily small in the close neighbourhood of zero. The viability of the ADKNN is verified via simulation example of nonlinear system.