Intelligent systems for the diagnosis and classification of Endocrine Myopathy (EM) plays very significant role in the medical field. Neuro-fuzzy system is refers to combinations of artificial neural networks and fuzzy logic, in which fuzzy system works like human reasoning and the learning structure of neural networks. The plan of this paper is to present the Neuro-fuzzy system for the classif...

computational intelligence approaches have gradually established themselves as a popular tool for forecasting the complicated financial markets. forecasting accuracy is one of the most important features of forecasting models; hence, never has research directed at improving upon the effectiveness of time series models stopped. nowadays, despite the numerous time series forecasting models propos...

We describe in this paper a new approach for pattern recognition using modular neural networks with a fuzzy logic method for response integration. We proposed a new architecture for modular neural networks for achieving pattern recognition in the particular case of human faces and fingerprints. Also, the method for achieving response integration is based on the fuzzy Sugeno integral with some m...

The inversion of a neural network is a process of computing inputs that produce a given target when fed into the neural network. The inversion algorithm of crisp neural networks is based on the gradient descent search in which a candidate inverse is iteratively refined to decrease the error between its output and the target. In this paper, we derive an inversion algorithm of fuzzified neural ne...

Abstract – This paper investigates the identification of nonlinear systems by neural networks. As the identification methods, Feedforward Neural Networks (FNN), Radial Basis Function Neural Networks (RBFNN), Runge-Kutta Neural Networks (RKNN) and Adaptive Neuro Fuzzy Inference Systems (ANFIS) based identification mechanisms are studied and their performances are comparatively evaluated on a thr...

In this paper, the impulsive fuzzy recurrent neural network with both time-varying delays and distributed delays is considered. Applying the idea of vector Lyapunov function, M-matrix theory and analytic methods, several sufficient conditions are obtained to ensure the existence, uniqueness and global exponential stability of equilibrium point for the addressed neural network. Moreover, the est...

Hyperknowledge is formed as a system of sets of interlinked concepts, much in the same way as hypertext is built with interlinked text strings; then hyperknowledgefunctions would be constructs which link concepts / systems of concepts in some predetermined or wanted way. Under hyperknowledge representation we mean the identification (or approximation) of hyperknowledge-functions in order to exp...

It has limitations to apply the traditional mathematical model to assess the risk of the information security for it is characterized by its nonlinearity and uncertainty. The RBF Neural Networks Theory, Particle Swarm Optimization (PSO) Analysis and Fuzzy Evaluation are combined to build a particle swarm optimizing model of Information Security Risk Assessment based on RBF Neural Networks, so a...