Intelligent fuzzy weighted input estimation method applied to inverse heat conduction problems

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.