The Study Of Intelligent Computation Methods And Their Applications In Fermentation Process

Intelligence can be looked as an compositive reflection of the accumulation and the application of the knowledge. It refers to the cognition of objectively existing things, the mastery of objective regulation and the abilitiy of making use of knowledge to solve practical problems. Artificial Intelligence (AI) just try to simulate the abilities of solving, inferring and learing as human beings by synthetic systems.After developing for nearly half an century, Artificial Intelligence contains many reasearch fileds. And the Intelligence Computation Method is one of the significant important fields. Thus many researchers have paid much attention on this field.In the past decades, Intelligence Computation has been studied wildly and achieved rapid developments. Morever, it has been arrived at fruitful achievements in the application of so many fields, such as signal processing, pattern recognition, system identification, fermentation control, bioinformatics, food science, medical treatment and business.In this dissertation, Intelligence Compution Methods and their applications in fermentation process modelling and bioinformatics have been deeply investigatied. They include Cascaded Centralized Takagi-Sugeno-Kang (CCTSK) fuzzy neural networks, Multi-Layer Perceptron, Radial Basis Function (RBF) neural networks, Takagi-Sugeno-Kang (TSK) fuzzy system and their applications in the fermentation process of Glutathione (GSH) and exopolysaccharide (EPS). Among them, we also do some reasearches on the reconstruction of the gene express regulatory network of E. coli in bioinformatics. To be concrete, the contributions of this dissertation are as follows:(1) We do some reasearches on fuzzy logic system and translate Uncertain Gaussian Mixture Model to an additive type-2 Takagi-Sugeno-Kang fuzzy logic system, and then we introduce a fuzzy-inference based fuzzy neural network, called the Cascaded Centralized Takagi-Sugeno-Kang fuzzy neural networks, for GSH fermentation process. It is well known that the data obtained from experiments unavoidly contain noises in the practical fermentation production, because of the complexity of fermentation process, the shortage of chemical apparatus and the limitation of the experimetal situation. Therefore, the covergence performance and the prediction accuracy of the GSH fermentation process modelling are often deteriorated by the noise existing in the experimental data. Moreover, the traditional model for the GSH fermentation process is usually lack of the interpretation. Since the CCTSK fuzzy neural network introduces the syllogism inference and centralized strategy, it is demonstrated that the modelling has a good robustness to the noise data and a high interpretation for the GSH fermentatiom process modeling, compared with the traditional fuzzy neural networks.(2) A robustness Radial Basis Function neural network model based on entropy criterion for the Glutathione fermentation process has been deeply investigated. Originated from the Parzen window density estimator and relative entropy for the sampling set, we propose a new criterion function, called entropy-based criterion function. Then the new criterion function is applied in the Radial Basis Function neural network model for the Glutathione fermentation process. Since the novel entropy-based criterion can be used to train the parameters of the RBF neural network model from the whole distribution structure of the training data set, which results in the fact that the Radial Basis Function neural network model method can have global approximation capability. Compared with the Mean Square Error 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 RBF neural network model have much better generalization ability and robustness for the Glutathione fermentation process.(3) We attempt to do furthur researches on entropy-based criterion function, and then incorporate this entropy-criterion based objective function into Multi-Layer Perceptron (MLP) model, Radial Basis Function (RBF) neural network model and Takagi-Sugeno-Kang (TSK) fuzzy system model for exopolysaccharide (EPS) fermentation from Lactobacillus. Our experimental results indicate that the three modeling methods mentioned above with entropy-criterion based objective function have obvious advantages over these with Mean Square Error criterion based objective function in the sense of approximation/generalization capability and robustness.The reason leading to such results may be that entropy-criterion based objective function is derived from the Parzen window density estimator and relative entropy, and it considers the whole distribution structure of the training set in the parameter's learning process, which is quite different from the MSE-criterion based objective function.(4) We also do some researches on the hot topic in bioinformatics fields, which is named as the reconstruction of gene express regulatory networks. Reconstruction of gene regulatory network is much significant to explore the essence of life. It is well known that the Linear Combination Model has been successfully applied to the reconstruction of the gene regulator network for its easy and fast solving. However, this model just takes the linear relationships between genes into account. In order to circumvent this problem, energy factor has been added in the Linear Combination Model, thus the model can be used to analysis the nonlinear relationships between genes. Then the proposed model has been applied to reconstruct the gene regulatory network of Escherichia coli on SOS DNA repair process. Our result demonstrates that the proposed model can reconstruct the SOS DNA repair process well and improve the accuracy.