ABSTRACT
Neural network computations are naturally expressed in matrix notation, and there are several toolboxes in the matrix language MATLAB, for example the commercial neural network toolbox, and a toolbox for identification and control. In the wider perspective of supervisory control, there are other application areas, such as robotic vision, planning, diagnosis, quality control, and data analysis (data mining). The strategy in this chapter is to aim at all these application areas, but only present the necessary and sufficient neural network material for understanding the basics. A few MATLAB simulated examples such as Coin detection, Pattern Recall, Pattern Classification, and Simulink models using different Neural Network architectures are illustrated in this chapter.
