using current employee performance data to predict the future behavior of the applicants is an interesting area which can broaden new horizons of knowledge lay in the organization. because of inherent ambiguity and uncertainty, cognitive limitations of the human mind make unknown behaviors of very complex systems difficult to predict. as a consequence, it is necessary to model the imprecise mod...

toxicity of 38 aliphatic carboxylic acids was studied using non-linear quantitative structure-toxicityrelationship (qstr) models. the adaptive neuro-fuzzy inference system (anfis) was used to construct thenonlinear qstr models in all stages of study. two anfis models were developed based upon differentsubsets of descriptors. the first one used log ow k and lumo e as inputs and had good predicti...

This paper presents an application of adaptive neuro-fuzzy (ANFIS) control for switched reluctance motor (SRM) speed. The ANFIS has the advantages of expert knowledge of the fuzzy inference system and the learning capability of neural networks. An adaptive neuro-fuzzy controller of the motor speed is then designed and simulated. Digital simulation results show that the designed ANFIS speed cont...

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...

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 novel adaptive neuro-fuzzy inference system based on interval Gaussian type-2 fuzzy sets in the antecedent part and Gaussian type-1 fuzzy sets as coefficients of linear combination of input variables in the consequent part. The capability of the proposed ANFIS2 for function approximation and dynamical system identification is remarkable. The structure of ANFIS2 is very sim...

limitations on freshwater resources have caused water resources managers to focus an increasing attention over the past few decades on water quality protection. surface water quality management in such resources as rivers, seas, lakes, and estuaries is of a greater importance than other water resources and a greater number of studies have been conducted on them as they are more accessible and, ...

This paper presents the architecture and learning procedure underlying ANFIS (Adaptive-Network-based Fuzzy Inference System), a fuzzy inference system implemented in the framework of adaptive networks. By using a hybrid learning procedure, the proposed ANFIS can construct an input-output mapping based on both human knowledge (in the form of fuzzy if-then rules) and stipulated input-output data ...

Chaos is a nonlinear behavior that shows chaotic and irregular responses to internal and external stimuli in dynamic systems. This behavior usually appears in systems that are highly sensitive to initial condition. In these systems, stabilization is a highly considerable tool for eliminating aberrant behaviors. In this paper, the problem of stabilization and tracking the chaos are investigated....

potato image segmentation is an important part of image-based potato defect detection. this paper presents a robust potato color image segmentation through a combination of a fuzzy rule based system, an image thresholding based on genetic algorithm (ga) optimization and morphological operators. the proposed potato color image segmentation is robust against variation of background, distance and ...