Research On Neural Networks & Fuzzy Systems And It's Application On Face Recognition

In recent years, neural network and fuzzy system have acquired prominent successes both in theoretical and applied aspects. However, there still exists many problems need to be solved in real world application. Several common problems are researched in this paper.For the commonly used three-layered neural network, how to select the number of the hidden nodes is always a real problem. This paper researches the three-layered B-spline neural network, which is commonly used in engineering and presents a constructive algorithm for selecting the number of the hidden nodes. It is proved that the proposes algorithm can be used to build a B-spline neural network with minimum hidden nodes to approximate any continuous function defined on compact set to a prescribed accuracy.Considering some application fields may take on hierarchical characteristics, this paper proposes a corresponding hierarchical radial basis function network (HRBFN) and its universal approximation property is proved. Compared with the classical RJBFN, HRBFN is more suitable for application fields with hierarchical characteristics. In addition, HRBFN can partially solve the problem of the rapid increasing of the hidden nodes when the dimension of input increasing.Fuzzy system can be used in non-linear system identification in two modes: one is series-parallel mode and the other is parallel mode. This paper researches the numeric approximation characteristic of series-parallel fuzzy system and points out that the number of fuzzy rules should not exceed the number of the samples. In addition, the influence of approximation error and system initial error on the performance of the series-parallel fuzzy system is also investigated. As to the parallel fuzzy system, this paper proves that as long as the parameters of parallel fuzzy system meet some prerequisites, the parallel prediction procedure converges and the parallel identification algorithm locally converges.In applying fuzzy systems, a common problem is that there may exist redundant fuzzy subsets and rules, which can on the one hand increase the complexity of the fuzzy system and wasting the computational capacity, on the other hand make it difficult to describe the fuzzy system with natural language. In this paper, merging algorithms of fuzzy subsets and rules are proposed to deal with TS fuzzy systems.These two algorithms can effectively reduce the number of fuzzy subsets and rules, thus greatly decrease the complexity and enhance the descriptive characteristic of the fuzzy system.This paper proposes a fuzzy neural model for face recognition. The architecture of the whole system takes structure of one-class-in-one-network (OCON), which has many advantages such as easy convergence, suitable for distribution application, quick retrieving, and incremental training. Novel methods are used to determine the number of fuzzy rules and initialize fuzzy subsets. The proposed approach has characteristics of quick learning/recognition speed, high recognition accuracy and robustness. Experiments on ORL demonstrate the effectiveness of the proposed approach and an average error rate of 3.95% is obtained.