 In order to simulate human beings’ thinking, this study is dedicated to proposing a novel fuzzy neural network (FNN) to cluster the fuzzy data being collected from the fuzzy questionnaires. The proposed FNN is the integration of adaptive resonance theory 2 (ART2) neural network and fuzzy sets theory. It can handle the fuzzy inputs as well as the fuzzy weights. A case study for mobile phone ma...

We propose Adaptive Fuzzy Neural Trees as an appropriate tool for intelligent data analysis, comprehension , and prediction. Instead of using a single technique Adaptive Fuzzy Neural Trees as a mixture of paradigms combine the main advantages of neural networks, decision trees, and fuzzy logic. Like neural networks they are able to model smooth functions and can be adapted incrementally. Like d...

Fuzzy approach and artificial neural networks become effective tool for researchers by forecasting fuzzy time series. The relation of these has advantage to improve forecasting performance especially in handling nonlinear systems. Hence, in this study we aimed to handle a nonlinear problem to apply neural network-based fuzzy time series model. Differing from previous studies, we used various de...

A fuzzy neural network, Falcon-MART, is proposed in this paper. This is a modi®cation of the original Falcon-ART architecture. Both Falcon-ART and Falcon-MART are fuzzy neural networks that can be used as fuzzy controllers or applied to areas such as forgery detection, pattern recognition and data analysis. They constitute a group of hybrid systems that incorporate fuzzy logic into neural netwo...

this work proposes a neural-fuzzy sliding mode control scheme for a hydro-turbine speed governor system. considering the assumption of elastic water hammer, a nonlinear mode of the hydro-turbine governor system is established. by linearizing this mode, a sliding mode controller is designed. the linearized mode is subject to uncertainties. the uncertainties are generated in the process of linear...

In this paper, we report on experiments in which we used neural networks for statistical anomaly intrusion detection systems. The five types of neural networks that we studied were: Perceptron; Backpropagation; PerceptronBackpropagation-Hybrid; Fuzzy ARTMAP; and Radial-Based Function. We collected four separate data sets from different simulation scenarios, and these data sets were used to test...