Using Evolutionary Programming Algorithm for Designing a Robust Neural Model for a Class of Control Systems

This study aims to design a neural model for a linear or nonlinear systems by using an Evolutionary Programming algorithm (EP) to choose the optimal structural construction for the network. We have used Matlab to design Neural Networks using (EP), because of its flexibility and ability to represent matrices (Cell Arrays, Multi Dimension Arrays). The experimental results confirm the efficiency with which this algorithm (EP) obtains the optimal network. We have tested the algorithm performance and the resulting model robustness by canceling one of the hidden layer nodes of the best net resulting from applying (EP). The effectiveness of that canceling on the resulting model output is also tested, and this study has shown the efficiency of the algorithm (EP) for the class of systems used.