The Design of Robust Soft Sensor Using ANFIS Network

Abstract

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.