ansarinezhad, abasali, Shahbazian, Mehdi (2017). Hybrid Adaptive Neuro-Fuzzy Inference System-Particle Swarm Optimization Model for Corrosion Prediction of 3C Steel Considering Different Marine Environment Factors. , 16(4), 35-41.
abasali ansarinezhad; Mehdi Shahbazian. "Hybrid Adaptive Neuro-Fuzzy Inference System-Particle Swarm Optimization Model for Corrosion Prediction of 3C Steel Considering Different Marine Environment Factors". , 16, 4, 2017, 35-41.
ansarinezhad, abasali, Shahbazian, Mehdi (2017). 'Hybrid Adaptive Neuro-Fuzzy Inference System-Particle Swarm Optimization Model for Corrosion Prediction of 3C Steel Considering Different Marine Environment Factors', , 16(4), pp. 35-41.
ansarinezhad, abasali, Shahbazian, Mehdi Hybrid Adaptive Neuro-Fuzzy Inference System-Particle Swarm Optimization Model for Corrosion Prediction of 3C Steel Considering Different Marine Environment Factors. , 2017; 16(4): 35-41.

Hybrid Adaptive Neuro-Fuzzy Inference System-Particle Swarm Optimization Model for Corrosion Prediction of 3C Steel Considering Different Marine Environment Factors

The Modares Journal of Electrical Engineering
Article 6, Volume 16, Issue 4, 2017, Pages 35-41    PDF (897.76 K)
Authors
abasali ansarinezhad; Mehdi Shahbazian*
Department of Instrumentation and Automation, Petroleum University of Technology, Ahwaz, Iran
Abstract
This research aims to describe a novel model, namely Hybrid Adaptive-Neuro Fuzzy Inference System-Particle Swarm Optimization (ANFIS-PSO), for predicting corrosion rate of 3C steel considering different marine environment factors. In the present research, five parameters (temperature, dissolved oxygen, salinity, pH, and oxidation–reduction potential) were used as input variables, with corrosion rate being the only output variable. In the proposed hybrid ANFIS-PSO model, the PSO served as a tool to automatically search for and update optimal parameters for the ANFIS, so as to improve generalizability of the model. Eeffectiveness of the hybrid model was then compared those to two other models, namely Adaptive-Neuro Fuzzy Inference System–Genetic Algorithm (ANFIS-GA) and Support Vector Regression (SVR) models, by evaluating their results against the same experimental data. The results showed that the proposed hybrid model tends to produce a lower prediction error than those of ANFIS-GA and SVR with the same training and testing datasets. Indeed, the hybrid ANFIS-PSO model provides engineers with an applicable and reliable tool to conduct real-time corrosion prediction of 3C steel considering different marine environment factors.
Keywords
corrosion prediction; Adaptive Neuro-Fuzzy Inference System (ANFIS); Particle Swarm Optimization (PSO); Support Vector Regression (SVR); Steel
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