Pattern Recognition Study On Combining Multiple Classifiers

As an important aspect in the domain of artificial intelligence, pattern recognition can extend the application range of computer and improve the ability of computers to perceive outside information. In this paper, research about how to improve recognition performance is presented.In the thesis, neural network pattern recognition based on BP algorithm is analysised and researched , and its disadvantage is pointed out. With the knowledge representation ability of fuzzy logic, we use fuzzy logic in neural network, and build up fuzzy neural network to improve network performance. Furthermore, We develop a kind of fuzzy neural network learning model based on Generic algorithm to solve the problem that how to decide the parameters of network and better performance can be achieved, while single classifier use single feature and has its limitation, improved network can't be get expected result.It's suggested that different classifier offered complementary information, which motivated the interest in combining classifiers to harness their strength. Some achievements are obtained, however most research focused on combination based abstract information and ignored the combination based on measured information, so complementary problem of classifiers hasn't been solved completely.for the above problem, a deep research about the combination of multiple classifier is presented, at first, we conclude the principle of combination multiple classifier and existed problem, and compare the different methods based on abstract information, also their drawback is pointed out. Then combination of measure information is described with fuzzy integral, and a dynamic evaluated method, bayes method, is to decide fuzzy integral density of model. Simulation shows efficiency of the method...