Study On Intelligent Fusion Algorithms Of Multi-source Information

In this article.Multi-source information systems provide a purposeful description of the environment that single source information can't offer Fusing several sources information increases the capability of intelligent system and yields more meaningful information, which is hard to be acquired by a single information source. Multi-source information fusion is a cross-science concerned with information science, computer science and automation science. The concept of information fusion is introduced at the beginning of this paper. the problems existing in fusion are analysed on the base of many references , and the future development of information fusion is predicted. The fusion structure is divided into four basic structures, which are centralized structure, distributed structure, hierarchical structure without feedback and hierarchical structure with feedback. Two fusion methods is proposed under different situations are proposed with evidence combination theory. One is the combination algorithm of results output by multi-classifier. The different classification feature of the same object is extracted and neural network classifier with different classification capability is designed separately,The genetic algorithm is applied to training the networks classifiers, output result of each neural network is thought of as evidence, Then the BPA of that evidence is determined. BPA must react the belief degree of different kind or same kind of different input. Evidence combination fusion is modified to deal with the conflicting information easily. The different capacity of each classifier is caused by different the classified feature. Feature input which cant be identified by one classifier may be identified by another. This paper says that model identification can be performed by multi-classifier, output result can be regarded as an evidence. Furthermore, we can determine the BPA of each classifier, then the precision of the model identification must be improved. Another is that multi-measurement can be made. An classified evidence by classifier can be obtained at each measurement, As the member of measurement increase4s, the accumulation of evidence increases, According to the object to be identified can be identified. A method of fusing uncertain information based on fuzzy integral is proposed. An intelligent fusion system is given, which can solve the FEI-DEO and DEI-DEOfusion problems. and is also effective for solving the problem of model identification for automatic highway operation by applying the intelligent fusion system to combining several feature index information. It enhances the capability of handling uncertain information. Two kinds of fuzzy neural networks, which are used to fuse multi-source information, are proposed by combing the advantages of neural networks and fuzzy inference, one is FMLPNN neural network, another is FBFNN neural networks. The training fuzzy neural networks fuses not only precious information but also imprecise and fuzzy data as well, which can solve the difficulty in getting fuzzy rules and membership function. The main advantage of fusing method with rough sets is that it does not require any prior or additional knowledge about the data. by analyzing uncertain, incomplete, and imprecise data, the fastest fusion algorithm is extracted, which can solve the problem of fusing over-loaded or incomplete information in multi-source information systems. The macroeconomic alarming model is established with statistical analysis, at first, the situations of economic operation of economic systems in different periods have been classified into five economic models by statistical analysis, then we have established the boundary cognition functions for each economic model, which provides the direct quantitative limit for the economic alarming.