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 ...

In this paper we describe Iris recognition using Modified Fuzzy Hypersphere Neural Network (MFHSNN) with its learning algorithm, which is an extension of Fuzzy Hypersphere Neural Network (FHSNN) proposed by Kulkarni et al. We have evaluated performance of MFHSNN classifier using different distance measures. It is observed that Bhattacharyya distance is superior in terms of training and recall t...

To make the modulation classification system more suitable for signals in a wide range of signal to noise rate (SNR), a novel method of designing combined classifier based on fuzzy neural network (FNN) is presented in this paper. The method employs fuzzy neural network classifiers and interclass distance (ICD) to improve recognition reliability. Experimental results show that the proposed combi...

This paper proposes a new hybrid time series forecasting system which is the fusion of the fuzzy system and the artificial neural network. The proposed fuzzy-neural system consists of 5 layers: the input layer, the fuzzification layer, the inference layer, the hidden layer, and the output layer. The artificial neural network is used as the fuzzy inference engine, while the genetic algorithm is ...

The paper presents the potential of fuzzy logic (FL-I) and neural network techniques (ANN-I) for predicting the compressive strength, for SCC mixtures. Six input parameters that is contents of cement, sand, coarse aggregate, fly ash, superplasticizer percentage and water-to-binder ratio and an output parameter i.e. 28day compressive strength for ANN-I and FL-I are used for modeling. The fuzzy l...

In this paper a novel neural fuzzy inference network (NFIN) it is proposed. The NFIN represent a modified Takagi-Sugeno-Kang (TSK) type fuzzy rule based model with neural network learning ability. The rules in the NFIN are created and adapted in an on-line learning algorithm. The structure learning together with the parameter learning forms the learning algorithms for the neural fuzzy network. ...

This chapter shows a new method of fuzzy network which can change the structure by the systems. This method is based on the self-organizing mapping (SOM) (Kohonen T. 1982), but this algorithm resolves the problem of the SOM which can’t change the number of the network nodes. Then, this new algorithm can change the number of fuzzy rules; it takes the experienced rules out of the necessary side f...

Recently, artificial neural networks (ANNs) have been extensively studied and used in different areas such as pattern recognition, associative memory, combinatorial optimization, etc. In this paper, we investigate the ability of fuzzy neural networks to approximate solution of a dual fuzzy polynomial of the form a1x+ ...+anx n = b1x+ ...+ bnx n+d, where aj , bj , d ε E 1 (for j = 1, ..., n). Si...

Fuzzy control systems and neural-network control systems for backing up a simulated truck, and truck-and-trailer, to a loading dock in a parking lot are presented. The supervised backpropagation learning algorithm trained the neural network systems. The robustness of the neural systems was tested by removing random subsets of training data in learning sequences. The neural systems performed wel...

Sentiment analysis is one part of natural language processing. can be done by lexicon based, or machine learning based. based on has advantage dynamism to meet with new datasets vocabulary. seeks understand the sentiments contained in a sentence. A sentence positive, neutral negative, its sentiments. have negative However, fact each does not always sentiment clearly. We try develop method that ...