Fermentation process modeling of exopolysaccharide using neural networks and fuzzy systems with entropy criterion

Zuo-Ping Tan, Shi-Tong Wang, Zhao-Hong Deng, Guo-Cheng Du

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DOI: 10.4236/jbise.2010.34059                

Abstract

The prediction accuracy and generalization of fermentation process modeling on exopolysaccharide (EPS) production from Lactobacillus are often deteriorated by noise existing in the corresponding experimental data. In order to circumvent this problem, a novel entropy-based criterion is proposed as the objective function of several commonly used modeling methods, i.e. Multi-Layer Perceptron (MLP) network, Radial Basis Function (RBF) neural network, Takagi-Sugeno-Kang (TSK) fuzzy system, for fermentation process model in this study. Quite different from the traditional Mean Square Error (MSE) based criterion, the novel entropy-based criterion can be used to train the parameters of the adopted modeling methods from the whole distribution structure of the training data set, which results in the fact that the adopted modeling methods can have global approximation capability. Compared with the MSE- criterion, the advantage of this novel criterion exists in that the parameter learning can effectively avoid the over-fitting phenomenon, therefore the proposed criterion based modeling methods have much better generalization ability and robustness. Our experimental results confirm the above virtues of the proposed entropy-criterion based modeling methods.

Keywords

Relative Entropy; MSE-Criterion Based Modeling; Robustness; Parzen Window; TSK Fuzzy System

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	Du, F., Lei, M. and Liu, Q. (2004) Advanced control in fermentation process. Chinese Journal of Information Technology in Construction, 33, 314-317.
[2]	Gao, X.J., Wang, P., Sun, C.Z., Zhang, Y.T., Zhang, H.Q. and Fan, Q.W. (2006) Modeling and optimization control for the microbial fermentation process. Chinese Journal of Control Science and Engineering, 13, 152-153.
[3]	Lee, M.W., Hong, S.H., Choi, H., Kim, J.H., Lee, D.S. and Park, J.M. (2008) Real-time remote monitoring of small-scaled biological wastewater treatment plants by a multivariate statistical process control and neural network-based software sensors. Process Biochemistry, 43, 1107-1113.
[4]	Desai, K.M., Vaidya, B.K., Singhal, R.S. and Bhagwat, S.S. (2005) Use of an artificial neural network in modeling yeast biomass and yield of b-glucan. Process Biochemistry, 40, 1617-26.
[5]	Kennedy, M.J., Prapulla, S.G. and Thakur, M.S. (1992) A comparison of neural networks to factorial design. Biotechnology Techniques, 6, 293-299.
[6]	Zhang, G.Y. and Fang, B.S. (2005) A model for amino acid composition and optimum pH in G/11 xylanase based on neural networks. Chinese Journal of Biotechnology, 21(4), 658-661.
[7]	Desai, K.M., Akolkar, S.K., Badhe, Y.P., Tambe, S.S. and Lele, S.S. (2006) Optimization of fermentation media for exopolysaccharide production from Lactobacillus plantarum using artificial intelligence based techniques. Process Biochemistry, 41, 1842-1848.
[8]	Tan, Z.P., Wang, S.T. and Du, G.C. (2008) Glutathione Fermentation Process Modeling based on CCTSK Fuzzy Neural Network. Journal of Biotechnology, 7, 73-79.
[9]	Yin, M., Zhang, X.H. and Dai, X.Z. (2000) Dissolved oxygen predictive control based on fuzzy neural networks for fermentation process. Chinese Journal of Control and Decide, 15, 523-526.
[10]	Feng, B. and Xu, W.B. (2006) Biochemical variable estimation model based on TSK fuzzy system. Chinese Journal of Applied Chemistry, 23, 343-346.
[11]	Meng, H., Fu, X.M. and Cao, G.P. (1999) Neural network fuzzy control of critric acid fermentation process. Chinese Hebei Journal of Industrial Science & Technology, 16, 53-55.
[12]	Bian, Z.Q., Zhang, X.G., Yan, P.F., Zhao, N.Y. and Zhang, C.S. (1999) Pattern recognition. Press of Tsing-hua University, Beijing.
[13]	Deng, Z.H. and Wang, S.T. (2004) RBF regression modeling based on visual system theory and weber law. Journal of Southern Yangtze University (Natural Science Edition), 3, 25-29.
[14]	Wang, S.H., Chung, F.L., Xu, M., Deng, Z.H. and Hu, D.W. (2007) A visual system theoretic cost criterion and its application to clustering. Information Technology Journal, 6, 310-324.
[15]	Kullback, S. (1968) Information theory and statistics. Dover Publications, New York.
[16]	Girolami, M. and He, C. (2003) Probability density estimation from optimally condensed data samples. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25, 1253-1264.
[17]	Erhan, G. and Jose, C.P. (2002) Information theoretic clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24, 158-171.
[18]	Gardner, W.A. (1984) Learning characteristics of stochastic-gradient-descent algorithms: A general study, analysis and critique. Signal Processing, 6, 113-133.
[19]	Jang, J.S.R., Sun, C.T. and Mizutani, E. (1997) A computational approach to learning and machine intelligence. Neuro-Fuzzy and Soft Computing, Upper Saddle River, NJ: Prentice-Hall.
[20]	Chung, F.L., Wang, S.T., Deng, Z.H. and Hu, D.W. (2006) CATSMLP: Towards a robust and interpretable multilayer perceptron with sigmoid activation functions. IEEE Transactions on Systems, Man, and Cybernetics-Part B, 36, 1319-1331.
[21]	Arena, P.L., Fortuna, R.R. and Xibilia, M.G. (1995) Multilayer perceptrons to approximate comples valued functions. International Journal of Neural Systems, 6, 435-446.
[22]	Chen, D.S. and Jain, R.C. (1994) A robust backpropagation learning algorithm for function approximation. IEEE Transactions on Neural Networks, 5, 467-479.
[23]	Park, J. and Sandberg, I.W. (1991) Universal approximation using radial-basis-function networks. Neural Computation, 3, 246-257.
[24]	Sugeno, M. and Yasukawa, T. (1993) A fuzzy-logic-based approach to qualitative modeling. IEEE Transactions on Fuzzy Systems, 1, 7-31.