Capability Of Adaptive Neuro-fuzzy Inference System Research Articles

Adaptive neuro-fuzzy inference system based data interpolation for particle image velocimetry in fluid flow applications

This paper presents an adaptive neuro-fuzzy inference system (ANFIS) approach for recovering the missing velocity vectors that commonly occur during fluid flow measurements in fluid mechanics. The capability of ANFIS in refilling the missing data is demonstrated with two case studies. First, the ANFIS is applied to estimate the velocity field data within the masked region of an available particle image velocimetry (PIV) experiment. Then, the ANFIS is trained using the data from outside the masked region and learns the relationship between the velocity vectors. Thus, it predicts the fluid patterns within the gappy area based on its understanding. ANFIS is also applied in another study to capture the small feature in the fluid flow. The vortices within a small area behind an obstacle are removed, and the rest of the data is introduced into the ANFIS model. The efficacy of the proposed ANFIS algorithm in predicting the missing velocity vectors is compared to both artificial neural network (ANN) and 2D cubic interpolation algorithms. It is found that intelligent algorithms (ANFIS and ANN) can predict the presence of vortices in the fluid flow even when there is no information about a circulating flow in the training dataset. The performance of ANFIS (R2= 0.91) in accurately predicting the velocity vectors is superior to the conventional ANN (R2= 0.86).

SOFT-COMPUTING ESTIMATION OF PRE-TREATED MANGO (MANGIFERA INDICA) KERNEL MOISTURE CONTENT IN A TRAY DRYER

This study presents Adaptive Neuro-fuzzy Inference System (ANFIS) technique for the estimation of pre-treated mango kernel moisture content in a tray dryer. The blanching and drying experiments were conducted at different drying air temperatures (45 -750C), drying time (0 - 420 minutes), blanching temperatures (40 - 1000C) and blanching time (3 - 9 minutes). The experimental input and output (moisture ratio) data were used to architect different ANFIS structures at different epoch numbers, input and output Membership Functions (MFs). The best structure for the estimation was achieved using trap input MF, constant output MF and 1500 epoch number. The Root Mean Squared Error (RMSE) and correlation coefficient R2 showed 0.0097 and 0.958 respectively for the ANFIS structure; furthermore, K value that compares ratio of the best ANFIS checking and training error is 0.87. The results of this investigation show the capability of ANFIS for the estimation of pre-treated mango kernel moisture content in a tray dryer.

Identification and Online Validation of a pH Neutralization Process Using an Adaptive Network-Based Fuzzy Inference System

In this study, the application of adaptive neuro-fuzzy inference system (ANFIS) architecture to build prediction models that represent the pH neutralization process is proposed. The dataset used to identify the process was obtained experimentally in a bench scale plant. The prediction model attained was validated offline and online and demonstrated as able to precisely predict the one step-ahead value of effluent pH leaving the neutralization reactor. The input variables were the current and one past value of the acid and base flow rates and the current value of the output variable. Variance accounted for (VAF) indices greater than 99% were achieved by the model in experiments in which the disturbances in the acid and basic solutions flow rates were applied separately. For tests with simultaneous disturbances, conditions never seen in the training and suffering from reactor level oscillations, the prediction model VAF index was still approximately 96%. The validations demonstrated the capability of ANFIS to build precise fuzzy models from input–output datasets. R2 values achieved were always larger than 0.96.