Artificial Neural Network with Hybrid Taguchi-genetic Algorithm for Nonlinear Mimo Model of Machining Processes

This paper developed an artificial neural network (ANN) model with hybrid Taguchi-genetic algorithm (HTGA) for the nonlinear multiple-input multiple-output (MIMO) model of machining processes. The HTGA in the MIMO ANN model finds the optimal parameters (i.e., weights of links and biases govern the input-output relationship of an ANN) by directly minimizing root-mean-squared error (RMSE), which is a key performance criterion. Experimental results show that the proposed MIMO HTGA-based ANN model outperforms the MIMO ANN methods with backpropagation (BP) algorithm given in the Matlab toolbox in terms of prediction accuracy for the nonlinear model of machining processes.