Abstract
Most of metallurgical performance testing devices use small high-temperature furnace to simulate physical environment for the sample testing. Since the controlled object has the dynamic characteristics of nonlinear, time-varying, large delay and large inertia during heating process, it is difficult to establish an accurate models to control thermal processes and optimize. This paper presents an adaptive neural fuzzy modeling approach based on T-S model for the heating process. Using the fuzzy system structure identification and parameter identification, the more accurate nonlinear model can be obtained. Duo to the fuzzy neural network has the capability of autonomous, quickly and effectively converging to the required relations of the input and output, the modeling accuracy has been improved. The simulation results demonstrate the effectiveness of the proposed algorithm, and the method can provide a reference for obtaining accurate nonlinear model.
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