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...

We consider some modifications of the neural gas algorithm. First, fuzzy assignments as known from fuzzy c-means and neighborhood cooperativeness as known from self-organizing maps and neural gas are combined to obtain a basic Fuzzy Neural Gas. Further, a kernel variant and a simulated annealing approach are derived. Finally, we introduce a fuzzy extension of the ConnIndex to obtain an evaluati...

This paper presents a novel hybridization of the fuzzy logic, the neural network and the coevolutionary algorithm for building a fuzzy-neural system (or a Mamdani fuzzy system) from data. The novel hybridization uses the coevolution of many species, and proposes the coevolution of groups of similar species, both for the optimization of the structure of the fuzzy-neural network. In the fuzzy-neu...

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...

This note analyzes the unsupervised fuzzy neural network (FNNU) of Kwan and Cai and finds the following: the FNNU is a clustering net, not a classifier net, and the number of clusters the network settles to may be less or more than the actual number of pattern classes—sometimes it could even be equal to the number of training data points! The huge number of connections in the FNNU can be drasti...

Neural networks, which make no assumption about data distribution, have achieved improved image classification results compared to traditional methods. Unfortunately, a neural network is generally perceived as being a ‘black box’. It is extremely difficult to document how specific classification decisions are reached. Fuzzy systems, on the other hand, have the capability to represent classifica...

The paper aims at developing new techniques for uncertainty management in expert systems for two generic class of problems using fuzzy Petri net that represents logical connectivity among a set of imprecise propositions. One class of problems addressed in the paper deals with the computation of fuzzy belief of any proposition from the fuzzy beliefs of a set of independent initiating proposition...

this paper is concerned with the development of a novel classifier for automatic mass detection of mammograms, based on contourlet feature extraction in conjunction with statistical and fuzzy classifiers. in this method, mammograms are segmented into regions of interest (roi) in order to extract features including geometrical and contourlet coefficients. the extracted features benefit from...

K e y w o r d s C o m p e n s a t o r y , Fuzzy similarity measure, Inverted wedge system, Backpropagation algorithm. 1. I N T R O D U C T I O N Recently, the neural fuzzy approach to system modeling has become a popular research topic [110]. Moreover, the neural fuzzy method possesses the advantages of both the pure neural and the fuzzy methods; it brings the low-level learning and computation...

Novel intelligent technique is a combination of fuzzy and neural network techniques that can be used to classify faults in electric power system protection. There have two problems in the protection system, which are: undesired tripping and fail to operate. Loss of power supply to relays and circuit breakers or failure in protective devices may cause failures in protection system. Construction ...