The Performance Analysis of Artificial Neural Network Based Shimizu-Morioka Chaotic System with Respect to Sample Numbers

In this paper, Shimizu-Morioka Chaotic System (SMCS) is modelled using Feed Forward Artificial Neural Network. In the realized network model, Log-Sigmoid and Purelin transfer functions have been used for hidden and output layer, respectively. 3-10-3 network structure is created using MATLAB. The model inputs are the state variables of SMCS. Outputs represent not only the outputs of SMCS but also iterative versions of these inputs. For the equations’ numeric solutions of describing SMCS, Runge Kutta 5 Butcher (RK-5-B) algorithm which is one of the differential equation solution methods, is used. Samples in the structure of described network, the created different numbers of samples using RK-5-B have been used as input data and performance analysis have been performed for these data. As a result, the paper shows that when the sample data numbers increase, network modeling performance gives more successful results.