In this paper two types of neural networks, namely the “traditional” tansig based neural networks and the multilayer perceptrons based on fuzzy flipflops (FNN) trained by the Bacterial Memetic Algorithm with Modified Operator Execution Order (BMAM) are tested and compared on their robustness to test functions outliers. The robust design of the FNN is presented, and the best suitable fuzzy neuro...

This paper deals with an impulsive stochastic fuzzy cellular neural networks with both time-varying and infinite distributed delays. Based on M-matrix theory and stochastic analysis technique, a sufficient condition is obtained to ensure the existence, uniqueness, and global exponential p−stability of the equilibrium point for the addressed impulsive stochastic fuzzy cellular neural network wit...

In order to overcome some unavoidable factors, like shift of the part, that influence the crisp neural networks’ recognition, the present study is dedicated in developing a novel fuzzy neural network (FNN), which integrates both the fuzzy set theory and adaptive resonance theory 2 (ART2) neural network for grouping the parts into several families based on the image captured from the vision sens...

In this paper, statistical, artificial neural networks and fuzzy based feature evaluation indices are analysed in order to determine the importance of prostate cancer prognostic markers. Seven prognostic markers are assessed in terms of 3 output classes using logistic regression as a statistical method, multilayer feedforward back propagation neural networks (MLFFBPNN) as a neural network tool,...

In this paper, the problem of stability analysis for a class of impulsive stochastic fuzzy Cohen-Grossberg neural networks with mixed delays is considered. Based on M-matrix theory and stochastic analysis technique, a sufficient condition is obtained to ensure the existence, uniqueness, and global exponential stability in mean square means of the equilibrium point for the addressed impulsive st...

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

In this paper, the discrete-time fuzzy BAM neural network with delays and impulses is studied. Sufficient conditions are obtained for the existence and global stability of a unique equilibrium of this class of fuzzy BAM neural networks with Lipschitzian activation functions without assuming their boundedness, monotonicity or differentiability and subjected to impulsive state displacements at fi...

The natural development of hybrid techniques causes biases with their roots in di erent technologies, in this case either in fuzzy systems or in neural networks. The neuro-fuzzy research is discussed in this paper giving examples and emphasising the neural network perspective. Introduction of new fuzzy systems models and the development of new neural learning algorithms could be observed in the...

To address deficiencies in the process of fault diagnosis of belt conveyor, this study uses a BP neural network algorithm combined with fuzzy theory to provide an intelligent fault diagnosis method for belt conveyor and to establish a BP neural network fault diagnosis model with a predictive function. Matlab is used to simulate the fuzzy BP neural network fault diagnosis of the belt conveyor. R...