The Study Of Several Issues In Neural-Fuzzy Pattern Recognition

Patten recognition is one of important tasks of artificial intelligence research, which has been extensively studied in the past tens of years. With the development of neural-fuzzy techniques, the neural-fuzzy techniques based pattern recognition techniques attract more and more attentions of the researchers and then a new research topic, i.e, the neural-fuzzy pattern reconition has emerged. Nowadays, a lot of important advancements have been achieved. However, the neural-fuzzy pattern recognition still confronts many challenges. Among of these challenges, several crucial challenges can be described as follows: 1)how to develop more robust neural-fuzzy pattern recognition algorithms; 2)how to develop the new-model based neural-fuzzy pattern recognition techniques; 3)how to apply the neural-fuzzy pattern recognition techniques to more extensive research fields, such as bioinformactis, computer vision and so on.Motivated by the above challenges, several issues are addressed in this study, which mainly involves the following three parts.In the first part, the robust neural-fuzzy pattern recognition techniques are investigated, which contains Chapter 2 - 5. In Chapter 2, in order to overcome the weakness of sensitivity to outliers of fuzzy cluster neural networks(FCNN), a robust fuzzy clusting neural networks algorithm RFCNN is presented. In Chapter 3, a novel robust maximum entropy clustering algorithm RMEC, as the improved version of the maximum entropy clustering algorithm MEC, is presented to overcome its drawbacks: very sensitive to outliers and uneasy to label them. In Chapter 4, the TSK fuzzy system modeling is re-considered from a new point of view and a more robust TSK fuzzy system modeling approach based on the visual-system principle and the Weber law is presented. In Chapter 5, we present a new MLP model called cascaded ATSMLP (CATSMLP) where the ATSMLPs are organized in a cascaded way. The proposed CATSMLP is proved to be functionally equivalent to a fuzzy inference system based on syllogistic fuzzy reasoning. Meanwhile, we in an indirect way indicate that the CATSMLP is more robust than the ATSMLP in an upper bound sense.In the second part, the ball-model based neural-fuzzy pattern recognition techniques are investigated, which contains Chapter 6 - 8. In Chapter 6, a novel fuzzy kernel hyper-ball perceptron is presented to realize the classification desicion. In Chapter 7, a novel classification machine called the minimax-probability based fuzzy hyper-ellipsoid machine MPFHM is proposed using the hyper-ellipsoid with the minimax probability principle and fuzzy concept. In Chapter 8, in order to overcome the shortcoming of the high time and space complexities of reduced set density estimator RSDE, a fast reduced set density estimator algorithm FRSDE is proposed. The finding that RSDE is equivalent to a special MEB problem is derived and with this finding the fast core-set-based MEB approximation algorithm is introduced to develop the algorithm FRSDE.In the third part, which contains Chapter 9, the applications of neural-fuzzy pattern recognition techniques in other research fields are investigated. In Chapter 9, in terms of the characteristics of elastic image registration, a fuzzy-inference-rule based flexible model is proposed for the automatic elastic image registration. Furthermore, we apply the proposed registration algorithm to visual tracking.