Modeling of pH neutralization process using fuzzy recurrent neural network and DNA based NSGA-II

In this paper, the Takagi–Sugeno fuzzy recurrent neural network (T–S FRNN) is applied to model a pH neutralization process. Since the accuracy and complexity of the network are two contradictory criteria for the T–S FRNN model, a DNA based NSGA-II is proposed to optimize the parameters of the model. In the DNA based NSGA-II, each individual is encoded with one nucleotide base sequence, modified DNA based crossover and mutation operators are designed to improve the searching ability of the algorithm, and crowding tournament selection is applied based on the Pareto-optimal fitness and the crowding distance. The study on the performance of test functions shows that the DNA based NSGA-II outperforms NSGA-II in the quality of the obtained Pareto-optimal solution. To verify the effectiveness of the established T–S FRNN model for the pH neutralization process, it is compared with two T–S FRNN models optimized with other methods. Comparison results show that the model optimized by DNA based NSGA-II is more accurate and the complexity of the network is acceptable.