Modeling Based on T-S Model for Thermal Process in Metallurgical Performance Testing

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.

• Periodical: Advanced Materials Research (Volumes 889-890)
• Pages: 699-702
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.889-890.699
• Online since: February 2014
• Authors: Dong Xie, Min Wang, Jing Liang Shi, Di Jian Xu, Hua Bing Wang
• Keywords: Adaptive Neural Network, Heating Process, Nonlinear, T-S Model

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