A fuzzified neural network copes with fuzzy signals and/or weights so that the information about the uncertainty of input and output can be served in the training process. Since learning process is the main function of fuzzy neural networks, in this study, we focus on review and comparison of the existing learning algorithms, so that the theoretical achievement and the application agenda of eac...

This paper studies traffic variable estimation, and presents a method of estimation for the number of vehicle waiting for queue (NVWQ) based on neuro-fuzzy at urban intersection. we present results of training the neural network for a detectorized intersection in Changsha City. The accuracy of NVWQ estimation using the fuzzy neural networks approaches is more than 90%. The fuzzy neural networks...

artificial neural networks (anns) are flexible computing frameworks and universal approximators that can be applied to a wide range of time series forecasting problems with a high degree of accuracy. however, despite of all advantages cited for artificial neural networks, they have data limitation and need to the large amount of historical data in order to yield accurate results. therefore, the...

In this paper, a new approach of modeling for Artificial Neural Networks (ANNs) models based on the concepts of fuzzy regression is proposed. For this purpose, we reformulated ANN model as a fuzzy nonlinear regression model while it has advantages of both fuzzy regression and ANN models. Hence, it can be applied to uncertain, ambiguous, or complex environments due to its flexibility for forecas...

The principal constituents of computational intelligence are fuzzy logic, neural networks and evolutionary algorithms, with emphasis in their mutual enhancement. The present paper reviews some applications of these formalisms in the area of medical image processing, where advantage is taken from the ability of fuzzy logic to work with imprecise information, the ability of neural networks to lea...

The paper considers both knowledge acquisition and knowledge interpretation tasks as tightly connected and continuously interacting processes in a contemporary knowledge engineering system. Fuzzy rules are used here as a framework for knowledge representation. An algorithm REFuNN for fuzzy rules extraction from adaptive fuzzy neural networks (FuNN) is proposed. A case study of Iris classificati...

In neural network the connection strength of each neuron is updated through learning. Through repeated simulations of crisp neural network, we propose the idea that for each neuron in the network, we can obtain reduced model with more efficiency using wavelet based multiresolution analysis (MRA) to form wavelet based quasi fuzzy weight sets (WBQFWS). Such type of WBQFWS provides good initial so...

This paper presents a fuzzy system that recognizes learning styles and emotions using two different neural networks. The first neural network (a Kohonen neural network) recognizes the student cognitive style. The second neural network (a back-propagation neural network) was used to recognize the student emotion. Both neural networks are being part of a fuzzy system used into an intelligent tuto...

In this paper, we propose the genetic design of fuzzy neural networks with multi-output based on interval type-2 fuzzy set (IT2FSFNNm) for pattern recognition. IT2FSFNNm is the networks of combination between the fuzzy neural networks (FNNs) and interval type-2 fuzzy set with uncertainty. The premise part of the networks is composed of the fuzzy partition of respective input spaces and the cons...