This paper presents a review of the central theories involved in hybrid models based on
fuzzy systems and artificial neural networks, mainly focused on supervised methods for …

MODEL-BASED FAULT DIAGNOSIS TECHNIQUES Page 1 CHAPTER2 MODEL-BASED FAULT
DIAGNOSIS TECHNIQUES 2.1 Introduction The model-based approach to fault detection in …

The amount of digital data in the universe is growing at an exponential rate with the rapid
development of digital information, and this reveals new machine learning methods …

This paper describes a general fuzzy min-max (GFMM) neural network which is a
generalization and extension of the fuzzy min-max clustering and classification algorithms of …

Due to the dynamic, distributed, and heterogeneous nature of today's networks, intrusion
detection systems (IDSs) have become a necessary addition to the security infrastructure …

A fuzzy min–max neural network based on data core (DCFMN) is proposed for pattern
classification. A new membership function for classifying the neuron of DCFMN is defined in …

In this paper, we introduce a novel family of multivariate fuzzy neural network interpolation
operators activated by sigmoidal functions belonging to the new class of multivariate …

The paper examines techniques for providing adaptation in intrusion detection and intrusion
response systems. As attacks on computer systems are becoming increasingly numerous …

Fault diagnosis and identification (FDI) have been widely developed during recent years.
Model-based methods, fault tree approaches and pattern recognition techniques are among …

This paper proposes a fuzzy min-max neural network classifier with compensatory neurons
(FMCNs). FMCN uses hyperbox fuzzy sets to represent the pattern classes. It is a supervised …