This paper presents a comparative analysis of the performance of fuzzy approaches on the task of predicting customer review ratings using a computational intelligence framework based on a genetic algorithm for data dimensionality reduction. The performance of the Fuzzy C-Means (FCM), a neurofuzzy approach combining FCM and the Adaptive Neuro Fuzzy Inference System (ANFIS), and the Simplified Fu...

In this paper, a novel neuro-fuzzy based method combined with a feature selection technique is proposed for online dynamic voltage stability status prediction of power system. This technique uses synchronized phasors measured by phasor measurement units (PMUs) in a wide-area measurement system. In order to minimize the number of neuro-fuzzy inputs, training time and complication of neuro-fuzzy ...

This paper presents a prediction model based on a new neuro-fuzzy algorithm for estimating time in construction projects. The output of the proposed prediction model, which is employed based on a locally linear neuro-fuzzy (LLNF) model, is useful for assessing a project status at different time horizons. Being trained by a locally linear model tree (LOLIMOT) learning algorithm, the model is int...

A new method for computing the resonant frequency of the circular microstrip antenna, based on the adaptive neuro-fuzzy inference system (ANFIS), is presented. A hybrid learning algorithm is used to identify the parameters of ANFIS. The results of the new method are in excellent agreement with the experimental results reported elsewhere.

In this paper, we present an on-line learning neuro-fuzzy system which was inspired by parts of the mechanisms in immune systems. It illustrates how an on-line learning neuro-fuzzy system can capture the basic elements of the immune system and exhibit some of its appealing properties. During the learning procedure, a neuro-fuzzy system can be incrementally constructed. We illustrate the potenti...

Fuzzy Decision Tree induction is a powerful methodology to extract human interpretable classification rules. The induction of fuzzy decision tree is done using popular Fuzzy ID3 algorithm [1, 2]. The other parameters which influence the performance of fuzzy decision tree are cut standard parameter  and leaf selection threshold parameter th  . Generally these parameters are to be selected heur...

This paper introduces a hybrid model of the neuro-fuzzy classifier with an integrated prediction of pilots’ mistakes. Experiments and studies of the network were conducted on real and test samples.The upgraded hybrid neuro-fuzzy classifier structure and the learning algorithm can solve the problem of the need for multiple individual performance measurements, the dynamics of which would make it ...