GA-based neural network to identification of nonlinear structural systems

The initial weights of neural network (NN) are randomly selected and thus the optimization algorithm used in the training of NN may get stuck in the local minimal. Genetic algorithm (GA) is a parallel and global search technique that searches multiple points, so it is more likely to obtain a global solution. In this regard, a new algorithm of combining GA and NN is proposed here. The GA is employed to exploit the initial weights and the NN is to obtain the network topology. Through the iterative process of selection, reproduction, cross over and mutation, the optimal weights can then be obtained. The proposed new algorithm is applied to the Duffing's oscillator and Wen's degrading nonlinear systems. Finally, the accuracy of this method is illustrated by comparing the results of the predicted response with the measured one.