ANFIS-Based Signal Reconstruction for Nonlinear Multifunctional Sensor
- 9 Downloads
In this paper, one approach based on adaptive network-based fuzzy inference system is designed to reconstruct signal for nonlinear multifunctional sensor used in multifunctional sensing technique. Before training, fuzzy rule number is automatically obtained through subtractive clustering to simplify the model structure. Simulation results for an analog circuit model which simulates a nonlinear multifunctional sensor system demonstrate the effectiveness of the designed approach in reconstructing signal. Application of processing real-life data reveals performance of the proposed method is evidently promoted by combining with fuzzy c-means clustering.
KeywordsMultifunctional sensing Signal reconstruction Nonlinearity ANFIS Subtractive clustering
The authors thank Prof. Guo Wei, Harbin Institute of Technology, for providing the real-life multifunctional sensor data.
This work is supported in part by the National Natural Science Foundation of China (61201364) and the Fundamental Research Funds for the Central Universities (NS2016034).
- 5.D. Liu, J. Sun, G. Wei, X. Liu, Application of moving least squares to multi-sensors data reconstruction. Acta Autom. Sin. 33(8), 823–828 (2007)Google Scholar
- 6.X. Liu, J. Sun, D. Liu, Nonlinear multifunctional sensor signal reconstruction based on support vector regression. Chin. J. Sens. Actuators 19(4), 1167–1170 (2006)Google Scholar
- 8.J. Liu, G. Wei, J. Sun, Signal reconstruction of nonlinear multifunctional sensor based on B-spline total least squares method. J. Data Acquis. Process. 28(3), 294–300 (2013)Google Scholar
- 21.R.P. Paiva, A. Dourado, B. Duarte, Applying subtractive clustering for neuro-fuzzy modeling of a bleaching plant, inProc. Eur. Control Conf. (Karlsruhe, Germany) (IEEE, 1999) pp. 4497–4502Google Scholar
- 22.X. Yu, F. Cheng, L. Zhu, Y. Wang, ANFIS modeling based on T-S model and its application for thermal process. Proc. Chin. Soc. Electr. Eng. 26(15), 78–82 (2006)Google Scholar
- 23.C.C. Kung, C.C. Lin, A new cluster validity criterion for fuzzy c-regression model and its application to T-S fuzzy model identification, in Proc. 2004 IEEE Int. Conf. Fuzzy Syst. (Budapest, Hungary), vol. 3 (IEEE 2004), pp. 1673–1678Google Scholar