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Journal of Instrumentation Technology. 2014, 2(1), 9-16
DOI: 10.12691/jit-2-1-3
Open AccessArticle

The Design of Robust Soft Sensor Using ANFIS Network

Hamed Hosseini1, , Mehdi Shahbazian1 and Mohammad Ali Takassi2

1Department of Instrumentation and Automation engineering, Petroleum University of Technology, Ahvaz, Iran

2Department of Basic Sciences, Petroleum University of Technology, Ahvaz, Iran

Pub. Date: June 04, 2014

Cite this paper:
Hamed Hosseini, Mehdi Shahbazian and Mohammad Ali Takassi. The Design of Robust Soft Sensor Using ANFIS Network. Journal of Instrumentation Technology. 2014; 2(1):9-16. doi: 10.12691/jit-2-1-3


A soft Sensor is a model which is used to estimate the unmeasurable output of an industrial process. Designing a soft sensor is usually difficult because its modeling is often based on case data. These data commonly contain the outliers and noise as soft sensor design is been problem. In order to solve the problem and successfully design a soft sensor, this paper introduces a new approach for designing a robust soft sensor which is not affected by outliers especially batch outlier and long tail noise. To response this goal, a robust soft sensor based on Adaptive Neuro-Fuzzy Inference System (ANFIS) which is based on robust cost function such as the summation of the absolute cost function. To minimize the cost function the particle swarm optimization (PSO) algorithm was used. The subtractive clustering technique was used to determine the ANFIS structure. The proposed method for designing a soft sensor is implemented on a chemical plant and compared with soft sensor based on ANFIS which is based on quadratic cost function. The simulation result shows higher accuracy in prediction of output variable in new robust soft sensor.

soft sensor ANFIS particle swarm optimization robust cost function outlier noise

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[1]  R. Neelakantan. and J. Guiver., “Applying Neural Networks,” Hydrocarbon Processing, vol. 9, pp. 114-119, 1998.
[2]  P. Kadlec., B. Gabrys., and S. Strandt., “Data-driven Soft Sensors in the process industry,” Computers and Chemical Engineering, vol. 33, pp. 795-814, 2009.
[3]  G. D. Gonzalez., “Soft sensors for processing plants,” in Proceedings of the second international conference on intelligent processing and manufacturing of materials, IPMM99, 1999.
[4]  L. Fortuna., S. Graziani., A. Rizzo., and M. G. Xibilia, “Soft Sensors for Monitoring and Control of Industrial Processes,” London: Springer-Verlag, 2007.
[5]  A. Adamski. and S. Habdank-Wojewodzki., “Traffic congestion and incident detector realized by fuzzy discrete dynamic system,” Archives of Transport, vol. 17, pp. 5-13, 2004.
[6]  W. B. Zhu., D. S. Li., and Y. Lu., “Real time speed measure while automobile braking on soft sensing technique,” Journal of Physics: Conference Series, vol. 48, pp. 730-733, 2006.
[7]  J. S. R. Jang., “ANFIS: Adaptive-network-based fuzzy inference systems,” IEEE Trans, Syst, Man Cybern, vol. 23, pp. 665-685, 1993.
[8]  S. Chiu., “Fuzzy Model Identification Based on Cluster Estimation,” Intelligent & Fuzzy Systems, vol. 2, 1994.
[9]  R. Kothandaraman. and L. Ponnusamy., “PSO tuned Adaptive Neuro-fuzzy Controller for Vehicle Suspension Systems,” Journal of Advances in Information Technology, vol. 3, pp. 57-63, Feb 2012.
[10]  K. Singh. and S. Upadhyaya., “Outlier Detection: Applications And Techniques” International Journal of Computer Science Issues, vol. 9, pp. 307-323, January 2012.
[11]  J. Kennedy. and R. Eberhart., “Particle Swarm Optimization,” Proceedings of IEEE International Conference on Neural Networks IV., pp. 1942-1948, 1995.
[12]  Y. Shi and R. Ebcrhart, “Paramctcr Sclcction in Panicle Swarm Optimization,” Proe. Scvcnth Annual Conf. on Evolutionary Programming, pp. 591-601, March 1998.
[13]  S. Kamelian., “Adaptive distributed control of industrial plants using stability-based nonlinear technique,” Ahwaz, 2012.