Applications of Sinusoidal Neural Network and Momentum Genetic Algorithm to Two-wheel Vehicle Regulating Problem

In an attempt to enhance the performance of neural network (NN), we propose a sinusoidal activation function for NN and apply a fast genetic algorithm (GA) with uses of momentum offspring (MOS) and constant‐range mutation (CRM) for training the NN. The proposed methods are aimed at designing a neurocontroller (NC) for regulating a two‐wheel vehicle system, known as nonholonomic system, in the viewpoint that it is necessary to improve the control process of the system even though several control methods, including applications of NN and GAs, have been developed. The learning performances of NCs are evaluated through the successful evolutionary rates of the control process based on the values of the squared errors. In order to compare the conventional methods with our proposed approaches and verify the effects of momentum GA on NC training, various numerical simulations will be carried out with different numbers of generations in GAs and different activation functions of NCs. Finally, the controllability of NC is investigated with certain sets of initial states. The simulations show that sinusoidal NC trained by momentum GA has a good performance regardless of the small values of population size and generations in GA. Copyright © 2007 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.