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...

Neuro-fuzzy computing, which provides efficient information processing capability by devising methodologies and algorithms for modeling uncertainty and imprecise information, forms at this juncture, a key component of soft computing. An integrated neuro-fuzzy system is simply a fuzzy inference system trained by a neural networklearning algorithm. The learning mechanism fine-tunes the underlying...

This report presents an optimized fuzzy neural network through the use of genetic algorithms. Fuzzy neural networks are widely used as it can adaptively deal with measurement of error directly, however, this neural model creates a dilemma from the fact that both large and small networks exhibit a number of disadvantages. If the network size is too small, the error rate tends to increase due to ...

This paper presents a fuzzy neural network approach to virtual product design. Contemporary design process requires the development of a new computational intelligent methodology that involves intelligent integration of design, analysis and evaluation, simulation and optimization in a virtual environment. In the paper, a soft-computing framework is developed for engineering design based on a hy...

The Hybrid neural Fuzzy Inference System (HyFIS) is a multilayer adaptive neural fuzzy system for building and optimizing fuzzy models using neural networks. In this paper, the fuzzy Yager inference scheme, which is able to emulate the human deductive reasoning logic, is integrated into the HyFIS model to provide it with a firm and intuitive logical reasoning and decision-making framework. In a...

In this paper we present NEFCLASS, a neuro{fuzzy system for the classiication of data. This approach is based on our generic model of a fuzzy perceptron which can be used to derive fuzzy neural networks or neural fuzzy systems for spe-ciic domains. The presented model derives fuzzy rules from data to classify patterns into a number of (crisp) classes. NEFCLASS uses a supervised learning algorit...

the proposed iafc neural networks have both stability and plasticity because theyuse a control structure similar to that of the art-1(adaptive resonance theory) neural network.the unsupervised iafc neural network is the unsupervised neural network which uses the fuzzyleaky learning rule. this fuzzy leaky learning rule controls the updating amounts by fuzzymembership values. the supervised iafc ...

Fuzzy logic, neural network, fuzzy-neural networks play an important role in the linguistic modeling of intelligent control and decision making in complex systems. The Fuzzy-Neural Network (FNN) learning represents one of the most effective algorithms to build such linguistic models. This paper proposes an Artificial Immune Algorithm (AIA) based optimal learning fuzzy-neural network (IM-FNN). T...

A type-2 Takagi-Sugeno-Kang fuzzy neural system is proposed and its parameter update rules are derived using fuzzy clustering and gradient learning algorithms. The proposed type-2 fuzzy neural system is used for the control and the identification of a real-time servo system. Fuzzy c-means clustering algorithm is used to determine the initial places of the membership functions to ensure that the...