Adaptive Neuro-fuzzy Inference System Algorithm Research Articles

Modeling of CCR5 antagonists as anti HIV agents using combined genetic algorithm and adaptive neuro-fuzzy inference system (GA–ANFIS)

Genetic algorithm-adaptive neuro-fuzzy inference system (GA–ANFIS) and GA–least square-support vector machine (GA–LS–SVM) as regression methods were investigated for building quantitative structure–activity relationships (QSAR). The experimental CCR5 inhibitory activities of 37 4-hydroxypiperidine derivatives were subjected to a QSAR analysis based on optimized structures calculated at the level of density functional theory using 6-31G(d,p) level. Results of QSAR model show that GA–ANFIS was better than the GA–LS–SVM method considerably in the goodness of fit and predictability parameters and other criteria for evaluation of the proposed models. These results reflect a nonlinear relationship between the selected descriptors by GA obtained from molecular structures and the inhibitory activity of the studied molecules.

Prediction of methane production in wastewater treatment facility: a data-mining approach

A prediction model for methane production in a wastewater processing facility is presented. The model is built by data-mining algorithms based on industrial data collected on a daily basis. Because of many parameters available in this research, a subset of parameters is selected using importance analysis. Prediction results of methane production are presented in this paper. The model performance by different algorithms is measured with five metrics. Based on these metrics, a model built by the Adaptive Neuro-Fuzzy Inference System algorithm has provided most accurate predictions of methane production.

Application of an expert system based on Genetic Algorithm–Adaptive Neuro-Fuzzy Inference System (GA–ANFIS) in QSAR of cathepsin K inhibitors

One strategy to potentially improve the success of drug design and development is to use chemometrics methods early in the process to propose molecules and scaffolds with ideal binding and to clarify physicochemical features influencing in their activity. Adaptive Neuro-Fuzzy Interference System (ANFIS) was used to construct the nonlinear quantitative structure–activity relationship (QSAR) model. The Genetic Algorithm (GA) was used to select descriptors which are responsible for the cathepsin K inhibitory activity of studied compounds. ANFIS regression is a nonlinear regression technique developed to relate many regressors to one or several response variables. The accuracy of the generated QSAR model (R2=0.916) is described using various evaluation techniques, such as leave-one-out procedure (RLOO2=0.875) and validation through an external test set (Rpred2=0.932).

A Novel Data Preprocessing Method for the Modeling and Prediction of Freeze-Drying Behavior of Apples: Multiple Output–Dependent Data Scaling (MODDS)

In the present study, the freeze drying behavior of apples have been modeled and predicted. Because freeze-drying is a very expensive and complex process, modeling of the freeze-drying process is a challenging task. In this study, a novel data scaling method called multiple output–dependent data scaling (MODDS) has been proposed and combined with an adaptive neuro-fuzzy inference system (ANFIS) to forecast the moisture content (MC), moisture ratio (MR), and drying rate (DR) values, which are outputs of freeze-drying behavior of apples. The input parameters of the freeze drying system are the sample thicknesses, drying time, pressure, relative humidity, chamber temperature, and sample temperature. Using the input parameters, the outputs of the freeze-drying process of apples were predicted using a hybrid system based on MODDS and ANFIS. In the first stage, only input parameters were scaled using MODDS. In the second stage, the outputs of freeze drying of apples were predicted with the scaled input parameters using ANFIS algorithm. Ninety-two samples were included in the data set, including 10-, 7-, and 5-mm samples. In order to evaluate the performance of the proposed model, the mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), coefficient of determination (R 2), index of agreement (IA), and mean absolute percentage error (MAPE) were used. Though MSE values of 2.48, 0.035, and 0.011 and IA values of 0.887, 0.887, and 0.466 were obtained for MC, MR, and DR, respectively, using the ANFIS prediction algorithm the hybrid MODDS-ANFIS model achieved MSE values of 0.003, 0.00005, and 0.00007 and IA values of 0.999, 0.999, and 0.993 for the prediction of MC, MR, and DR, respectively. The results obtained demonstrate that the proposed hybrid system is a robust and efficient method for the modeling and prediction of freeze-drying behavior of apples.

Fuzzy models to predict consumer ratings for biscuits based on digital image features

Fuzzy models to recognize consumer preferences were developed as part of an automated inspection system for biscuits. Digital images were used to estimate the physical features of chocolate chip cookies including size, shape, baked dough color, and fraction of top surface area that was chocolate chips. Polls were conducted to determine consumer ratings of cookies. Four fuzzy models were developed to predict consumer ratings based on three of the features. There was substantial variation in consumer ratings in terms of individual opinions, as well as poll-to-poll differences. Parameters for the inference system, including fuzzy values for cookie features and consumer ratings, were defined based on the judgment and statistical analysis of data from the calibration polls. The two fuzzy models that gave satisfactory estimates of average consumer ratings are: the Mamdani inference system based on eight fuzzy values for consumer ratings; and the Sugeno inference system developed using the adaptive neurofuzzy inference system algorithm.