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

Abstract 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.

[1]  Mohammad Fazle Azeem,et al.  Parameter identification of TSK neuro-fuzzy models , 2011, Fuzzy Sets Syst..

[2]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[3]  Jamal A. Abdalla,et al.  Modeling and simulation of low-cycle fatigue life of steel reinforcing bars using artificial neural network , 2011, J. Frankl. Inst..

[4]  Ning Wang,et al.  A DNA based genetic algorithm for parameter estimation in the hydrogenation reaction , 2009 .

[5]  Bing Chen,et al.  Mean square exponential stability of stochastic fuzzy Hopfield neural networks with discrete and distributed time-varying delays , 2009, Neurocomputing.

[6]  Hannu T. Toivonen,et al.  Neural network approximation of a nonlinear model predictive controller applied to a pH neutralization process , 2005, Comput. Chem. Eng..

[7]  Surjya K. Pal,et al.  Modeling of electrical discharge machining process using back propagation neural network and multi-objective optimization using non-dominating sorting genetic algorithm-II , 2007 .

[8]  Ning Wang,et al.  A protein inspired RNA genetic algorithm for parameter estimation in hydrocracking of heavy oil , 2011 .

[9]  Chen Xiao-ming.  Multi-objective optimization based FRNN and its application to pH control process , 2009 .

[10]  Ieroham S. Baruch,et al.  A fuzzy-neural multi-model for nonlinear systems identification and control , 2008, Fuzzy Sets Syst..

[11]  Dale E. Seborg,et al.  Adaptive nonlinear control of a pH neutralization process , 1994, IEEE Trans. Control. Syst. Technol..

[12]  Karim Salahshoor,et al.  Online affine model identification of nonlinear processes using a new adaptive neuro-fuzzy approach , 2012 .

[13]  Joao P. Hespanha,et al.  Multi-model adaptive control of a simulated pH neutralization process , 2007 .

[14]  Ai Poh Loh,et al.  Modeling pH neutralization processes using fuzzy-neural approaches , 1996, Fuzzy Sets Syst..

[15]  Jili Tao,et al.  DNA computing based RNA genetic algorithm with applications in parameter estimation of chemical engineering processes , 2007, Comput. Chem. Eng..

[16]  Davut Hanbay,et al.  Modeling switched circuits based on wavelet decomposition and neural networks , 2010, J. Frankl. Inst..

[17]  Ali Belmehdi,et al.  Multi-objective optimization of TSK fuzzy models , 2008, 2008 5th International Multi-Conference on Systems, Signals and Devices.

[18]  Bing Chen,et al.  Robust Stability for Uncertain Delayed Fuzzy Hopfield Neural Networks With Markovian Jumping Parameters , 2009, IEEE Trans. Syst. Man Cybern. Part B.

[19]  Dale E. Seborg,et al.  Nonlinear internal model control strategy for neural network models , 1992 .

[20]  Dale E. Seborg,et al.  Modelling and Self-Tuning Control of a Multivariable pH Neutralization Process Part I: Modelling and Multiloop Control , 1989, 1989 American Control Conference.

[21]  Agustín Jiménez,et al.  A new approach to fuzzy estimation of Takagi-Sugeno model and its applications to optimal control for nonlinear systems , 2012, Appl. Soft Comput..

[22]  Honghai Liu,et al.  Reliable Fuzzy Control for Active Suspension Systems With Actuator Delay and Fault , 2012, IEEE Transactions on Fuzzy Systems.

[23]  Jiuhui Qu,et al.  New bipolar electrocoagulation–electroflotation process for the treatment of laundry wastewater , 2004 .

[24]  Masoud Sadeghian,et al.  Identification, prediction and detection of the process fault in a cement rotary kiln by locally linear neuro-fuzzy technique , 2011 .

[25]  Ashutosh Tewari,et al.  Knowledge-based parameter identification of TSK fuzzy models , 2010, Appl. Soft Comput..

[26]  Robert Babuska,et al.  Neuro-fuzzy methods for nonlinear system identification , 2003, Annu. Rev. Control..

[27]  A. Xue,et al.  Dynamic Modeling and Nonlinear Predictive Control Based on Partitioned Model and Nonlinear Optimization , 2011 .

[28]  R. Scherer.  Neuro-fuzzy relational systems for nonlinear approximation and prediction , 2009 .

[29]  M Hagiya,et al.  A DNA-based in vitroGenetic Program , 2002, Journal of biological physics.