Transit Articles Extraction Based on Domestic Fusion Algorithm

An elaborately designed software architecture is put forward based on fuzzy sets theory (FST), which is specialized in multiple sensor fusion and mechanism failure diagnosis. Besides, when confronted with multiple fault signals, fusion parameters can be dynamically adapted based on principles of fuzzy soft clustering so as to promote immune ability in artificially mechanical systems. The key point in this new approach lies in its power on faults detection, which requires no prior information on the state vectors of the sensors and system behavior, and no supplemental machine learning operation is required. The proposed algorithm combines principles of artificial immune system and the classical technique in fuzzy theory, which will consist of two main portions. In the first part a traditional data fuse structure is constructed, the sensor signals will be fed into it to implement the fuzzy aggregating algorithm.

[1]  R.K. Saha,et al.  Track-to-track fusion with dissimilar sensors , 1996, IEEE Transactions on Aerospace and Electronic Systems.

[2]  P. Valin,et al.  Position and attribute fusion of radar, ESM, IFF and Datalink for AAW missions of the Canadian Patrol Frigate , 1996, 1996 IEEE/SICE/RSJ International Conference on Multisensor Fusion and Integration for Intelligent Systems (Cat. No.96TH8242).

[3]  B. Palaniappan,et al.  An investigation of neuro-fuzzy systems in psychosomatic disorders , 2005, Expert Syst. Appl..

[4]  Murali Tummala,et al.  Multirate, multiresolution, recursive Kalman filter , 2000, Signal Process..

[5]  Ilke Turkmen IMM fuzzy probabilistic data association algorithm for tracking maneuvering target , 2008, Expert Syst. Appl..

[6]  Robert Sutton,et al.  Adaptive tuning of a Kalman filter via fuzzy logic for an intelligent AUV navigation system , 2004 .

[7]  D. J. Peters,et al.  Fusion of IFF and radar data , 1996, Proceeding of 1st Australian Data Fusion Symposium.

[8]  Ivica Kostanic,et al.  Principles of Neurocomputing for Science and Engineering , 2000 .

[9]  Hassan B. Kazemian Study of Learning Fuzzy Controllers , 2001, Expert Syst. J. Knowl. Eng..

[10]  S. M. Smith,et al.  Enhancement of the inertial navigation system for the Morpheus autonomous underwater vehicles , 2001 .

[11]  Sh. Ataei,et al.  Sensor fusion of a railway bridge load test using neural networks , 2005, Expert Syst. Appl..