Cancer research is one of the major research areas in the medical field. Adaptive Neuro Fuzzy Interference System is used for the classification of Cancer. This algorithm compared with proposed algorithm of Adaptive Neuro Fuzzy Interference system with Runge Kutta learning method for the best classification of cancer. It is one of the better techniques for the classification of the cancer. The ...

introduction: kidney disease is a major public health challenge worldwide. epidemiologic data suggest a significant relationship between underlying diseases and decrease in glomerular filtration rate (gfr). clinical studies and laboratory research have shown that the mentioned parameter is effective in development and progression of the renal disease per se. in this study, we used learning-base...

Proper models for prediction of time series data can be an advantage in making important decisions. In this study, we tried with the comparison between one of the most useful classic models of economic evaluation, auto-regressive integrated moving average model and one of the most useful artificial intelligence models, adaptive neuro-fuzzy inference system (ANFIS), investigate modeling procedur...

some applications are critical and must designed Fault Tolerant System. Usually Voting Algorithm is one of the principle elements of a Fault Tolerant System. Two kinds of voting algorithm are used in most applications, they are majority voting algorithm and weighted average algorithm these algorithms have some problems. Majority confronts with the problem of threshold limits and voter of weight...

some applications are critical and must designed Fault Tolerant System. Usually Voting Algorithm is one of the principle elements of a Fault Tolerant System. Two kinds of voting algorithm are used in most applications, they are majority voting algorithm and weighted average algorithm these algorithms have some problems. Majority confronts with the problem of threshold limits and voter of weight...

Application of a self-learning adaptive network-based fuzzy inference system as a power system stabilizer (PSS) is described in this paper. A multilayer adaptive network is employed to design the fuzzy logic controller with self-learning capability that does not require another controller to tune the fuzzy inference rules and membership functions. Details of the design process are given. Behavi...

In this paper, several neural network and statistical learning approaches are proposed that learn to make human like decisions for the job assignment problem of the US Navy. Comparison study of Feedforward Neural Networks (FFNN), Adaptive Neuro-Fuzzy Inference System (ANFIS), Support Vector Machine (SVM) and Adaptive Bayes (AB) classifier with Generalized Estimation Equation (GEE) is provided. ...

This study suggests new learning laws for Adaptive Network based Fuzzy Inference System that is structured on the basis of TSK type III as a system identifier. Stable learning algorithms for consequence parts of TSK type III rules are proposed on the basis of the Lyapunov stability theory and some constraints are obtained. Simulation results are given to validate the results. It is shown that i...

A multi objective Honey Bee Mating Optimization (HBMO) designed by online learning mechanism is proposed in this paper to optimize the double Fuzzy-Lead-Lag (FLL) stabilizer parameters in order to improve low-frequency oscillations in a multi machine power system. The proposed double FLL stabilizer consists of a low pass filter and two fuzzy logic controllers whose parameters can be set by the ...