Research On Information Fusion And Intelligent Process

Some aspects in the area of multisensor information fusion and intelligent information process are studied in this paper.The application of multiserisor fusion to the navigation of mobile robots is studied first. The fusion problem for self-localization is discussed. The mobile robot uses the photoelectric code-recorder to localize itself. At the same time, the Extended Kalman Filter is employed to fuse the multiple ultrasonic sensors, result of which is used to reset the photoelectric code recorder by the retroactive algorithm. The algorithm can not only eliminate the influence of the cumulative errors of the photoelectric code recorder, but also it can satisfy the requirement of the real-time control. A direct inverse model controller of fuzzy neural network with changeable structure based on Takagi-Sugeno inference is presented and it is used to the motion control of mobile robot. In order to avoid the obstacles successfully, detection results from CCD and ultrasonic sensors are fused by a fuzzy neural network, which acts as an avoidance controller. Simulation results show the validity of the proposed methods in the navigation of mobile robots.Multisensor information is fused in temporal field by combing Dempster-Shafer theory and neural networks in order to conduct recognition and classification tasks. The algorithm makes full use of advantages of both theories. The simulation shows that the method can effectively enhance the rate of the workpiece identification.Algorithms to combine the neural networks classifiers based on Dempster-Shafer theory and two kinds of fuzzy integral (Sugeno and Choquet integral ) respectively are proposed. The influences of the fact that every classifier has different classification ability for different class are all considered in these two kinds of algorithms. Several databases of UCI repository and a multiple sensor fusion system for workpiece identification are tested, showing the validity of these algorithms.The main advantage of rough sets data analysis is that it doesn't require any prior or additional knowledge about the data, which is then used in this paper to analysis the database, acquiring automatically the hierarchical rule sets. In order to ensure maximum consistency of the quantiflcational data, the genetic algorithms is used to get the optimal number and points of division of quantification intervals. At the same time the quantification intervals is fuzzified and crisp rule sets are then transformed to fuzzy rule sets. Then the fuzzy inference is conducted to enhance the robustness. The validity of the proposed algorithm is proved through the test on some databases of the UCI repository.The concept of rough entropy is proposed. The monotony between the uncertainty of knowledge in the rough set theory and its corresponding rough entropy is proved. The rough entropy of the uncertainty of ordinary set and fuzzy set, and the monotonous relation between the uncertainty of these two kinds of set and their corresponding rough entropy, are discussed. Using the concept of rough entropy, the essence of the uncertainty can be fully reflected from the viewpoint of information. At last an entropy based attribute reduct algorithm is proposed. It is used to the attribute reduct of Iris and Hsv database, and the ideal results can be achieved.