Intelligent fuzzy weighted input estimation method applied to inverse heat conduction problems |
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| Authors: | Tsung-Chien Chen Ming-Hui Lee |
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| Affiliation: | 1. Department of Power Vehicle and Systems Engineering, Chung Cheng Institute of Technology, National Defense University, Ta-shi, 190, Sanyuan 1st Street, Tao-Yuan 335, Taiwan, ROC;2. School of Defense Science, Chung Cheng Institute of Technology, National Defense University, Ta-shi, 190, Sanyuan 1st Street,Tao-Yuan 335, Taiwan, ROC;1. Technological University of Panama, School of Mechanical Engineering, Panama City, Panama;2. Osaka University, Joining and Welding Research Institute, Suita, Osaka, Japan;1. Department of Psychiatry, Kuopio University Hospital and University of Eastern Finland, Kuopio, Finland;2. Department of Physical and Rehabilitation Medicine, Mikkeli Central Hospital, Mikkeli, Finland;3. Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Finland;4. Kyyhkylä Rehabilitation Center, Mikkeli, Finland;5. Department of Physical Medicine and Rehabilitation, Kuopio University Hospital, Kuopio, Finland;1. Laboratoire d’Etude des Microstructures et de Mécanique des Matériaux, LEM3, Université de Lorraine, Ile du Saulcy, 57045 Metz Cedex, France;2. Laboratoire d’Energétique et de Mécanique Théorique et Appliquée, LEMTA CNRS-UMR 7563, Université de Lorraine-InSIC, 27 Rue d’Hellieule, 88100 Saint-Dié-des-Vosges, France;3. CIRTES, Centre Européen de Prototypage et Outillage Rapide, 29 bis rue d’Hellieule, 88100 Saint-Dié-des-Vosges, France |
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| Abstract: | The innovative intelligent fuzzy weighted input estimation method which efficiently and robustly estimates the unknown time-varying heat flux in real-time is presented in this paper. The algorithm includes the Kalman Filter (KF) and the recursive least square estimator (RLSE), which is weighted by the fuzzy weighting factor proposed based on the fuzzy logic inference system. To directly synthesize the Kalman filter with the estimator, this work presents an efficient robust forgetting zone, which is capable of providing a reasonable compromise between the tracking capability and the flexibility against noises. The capability of this inverse method are demonstrated in one- and two-dimensional time-varying estimation cases and the proposed algorithm is compared by alternating between the constant and adaptive weighting factors. The results show that this method has the properties of faster convergence in the initial response, better target tracking capability and more effective noise reduction. |
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