We consider a generalized model of neural network with a fuzziness and chaos. The origin of the fuzzy signals lies in complex biochemical and electrical processes of the synapse and dendrite membrane excitation and the inhibition mechanism. The mathematical operations included into fuzzy neural network modeling are: the scalar product between inputs of layers and synaptic weights is replaced by...

monitoring and forecasting of air quality parameters in urban areas is considered as one of the challenges of the human environment. it depends on several factors such as topography, climate, population and transport network which the interaction of these spatial factors has been as a dynamic phenomenon, non-linear and ambiguous. in this study, two models suggested to predict and modeling conce...

This document describes the architecture of neuro fuzzy systems. First part of the document provides a review of general notions of fuzzy logic, and structure of fuzzy systems. A procedure and different types of fuzzy reasoning are described. The second part is devoted to the neural networks and to the simplest model of artificial neuron. In the third part, the similarities and differences betw...

Background and aims: Depression disorder is one of the most common diseases, but the diagnosis is widely complicated and controversial because of interventions, overlapping and confusing nature of the disease. So, keeping previous patients’ profile seems effective for diagnosis and treatment of present patients. Use of this memory is latent in synthetic neuro-fuzzy algorithm. P...

A Recurrent Trainable Neural Network (RTNN) with a two layer canonical architecture and a dynamic Backpropagation learning method are applied for local identification and local control of complex nonlinear plants. The RTNN model is incorporated in Hierarchical Fuzzy-Neural Multi-Model (HFNMM) architecture, combining the fuzzy model flexibility with the learning abilities of the RTNNs. A direct ...

The various applications of VLSI circuits in highperformance computing, telecommunications, and consumer electronics has been expanding progressively, and at a very hasty pace. This paper describes a new model for partitioning a circuit using DBSCAN and fuzzy ARTMAP neural network. The first step is concerned with feature extraction, where we had make use DBSCAN algorithm. The second step is th...

A genetic algorithm (GA) based recurrent fuzzy neural network modeling method for dynamic nonlinear chemical process is presented. The dynamic recurrent fuzzy neural network (RFNN) is constructed in terms of Takagi-Sugeno fuzzy model. The consequent part is comprised of the dynamic neurons with output feedback. The number and the parameters of membership functions in the premise part are optimi...

Fuzzy neural networks have several features that make them well suited to a wide range þÿ o l knowledge engineering applications. These strengths include fast and accurate learning, good generalisation capabilities, excellent explanation facilities in the fonn of semanticallymeaningful fuzzy rules, and the ability to accommodate both data and existing expert knowledge about the problem under co...

-Fuzzy theory is integrated with Artificial Neural Network to create a bridge safety assessment model, through which the Fuzzy-Neural Network is improved in the light of sample data simulation. First, determine network layers interms of the seven critiria for bridge safety assessment. Then enter sample data at the input layer; study sample at the fuzzy reasoning layer by BP calculation method; ...

The aim of the paper is to introduce a concurrent fuzzy neural network approach, representing a winner-takes-all collection of fuzzy Gaussian modules. Our proposed model will be applied for the pattern classification. The fuzzy neural model consists of a set of M fuzzy neural networks, one for every class, each network having a single output. The output value corresponding to the M k k , 1 ,  ...