Reliability forecasting for industrial robot based on genetic algorithm and RBF neural network

The key that the robot is practical lies in its credibility and safety. Therefore, reliability forecasting for industrial robot is studied. RBF can be regarded as a data modeling technique for high-dimensional space and a universal approximation scheme, which is a powerful technique for generating multivariate nonlinear mapping. The performance of RBF neural network replies on the choice of RBF center and width, and linear weights connecting the hidden lay and the output layer. Therefore, genetic algorithm is used to select RBF center and width, and linear weights connecting the hidden lay and the output layer in radial basis function neural network in the paper. Thus, the hybrid method of genetic algorithm and RBF neural network is applied to reliability forecasting for industrial robot. The testing results illustrate that the proposed GA_RBFNN has better forecasting accuracy and computational efficiency for reliability of industrial robot than the other methods.