introduction: the adaptive neuro-fuzzy inference system (anfis) is a soft computing model based on neural network precision and fuzzy decision-making advantages, which can highly facilitate diagnostic modeling. in this study we used this model in breast cancer detection. methodology: a set of 1,508 records on cancerous and non-cancerous participant’s risk factors was used.  first, the risk fact...

It is crucial to detect disease complications caused by metabolic syndromes early. High cholesterol, high glucose, and blood pressure are indicators of syndrome. The aim this study use adaptive neuro fuzzy inference system (ANFIS) predict potential compare its performance other classifiers, namely random forest (RF), C4.5, naïve Bayesian classification (NBC) algorithms. Fuzzy subtractive cluste...

Wind energy is increasing its participation as a main source of energy in power grids and electric utility systems around the world. One of the main difficulties of integrating large amounts of wind energy in power grids is the natural intermittency of its generated power [1, 2] due to the energy produced from the wind turbine being dependent on the availability of the wind, which is highly sto...

When a fault occurs during an industrial inspection, workmen have to manually find the location and type of the fault in order to remove it. It is often difficult to accurately find the location and type of fault. Hence, development of an offline intelligent fault diagnosis system for process control industry is of great importance since successful detection of fault is a precursor to fault iso...

Introduction: The adaptive neuro-fuzzy inference system (ANFIS) is a soft computing model based on neural network precision and fuzzy decision-making advantages, which can highly facilitate diagnostic modeling. In this study we used this model in breast cancer detection. Methodology: A set of 1,508 records on cancerous and non-cancerous participant’s risk factors was used.  First,...

prediction, diagnosis, recovery and recurrence of the breast cancer among the patients are always one of the most important challenges for explorers and scientists. nowadays by using of the bioinformatics sciences, these challenges can be eliminated by using of the previous information of patients records. in this paper has been used adaptive nero fuzzy inference system and data mining techniqu...

This paper presents a new hybrid methodology for learning Sugeno-type fuzzy models via subtractive clustering, Adaptive Boosting Regression (AdaBoostR) and Unscented Kalman Filter (UKF). The generated fuzzy models are used for modeling nonlinear benchmark processes. In the proposed procedure, first one fuzzy rule is generated by subtractive clustering algorithm from available data of a given no...

support vector regression (svr) is a learning method based on the support vector machine (svm) that can be used for curve fitting and function estimation. in this paper, the ability of the nu-svr to predict the catalytic activity of the fischer-tropsch (ft) reaction is evaluated and the result is compared with two other prediction techniques including: multilayer perceptron (mlp) and subtractiv...

Typhoon intensity forecast is an important issue. The objective of this study to construct a 5-day 12-hourly typhoon model based on the adaptive neuro-fuzzy inference systems (ANFIS) improve in Northwest Pacific. It analyzed improvement ANFIS by comparing it with MLR when only atmospheric factor or both and oceanic factors are considered. This collected SHIPS (Statistical Hurricane Intensity Pr...

This study aims to develop an Adaptive Network-based Fuzzy Inference System technique (ANFIS) and using the parameters of a complex mathematical model in the RO membrane performances. The ANFIS was constructed by using a subtractive clustering method to generate initial fuzzy inference systems. The model trained by 70% of the data set and then its validity is examined by remained 30% data set. ...