In this paper, a real-coded genetic algorithm (GA) is proposed capable of simultaneously optimizing the structure of a system (number of inputs, membership functions and rules) and tuning the parameters that de®ne the fuzzy system. A multideme GA system is used in which various fuzzy systems with di€erent numbers of input variables and with di€erent structures are jointly optimized. Communicati...

We have been using fuzzy data mining techniques to extract patterns that represent normal behavior for intrusion detection. In this paper we describe a variety of modifications that we have made to the data mining algorithms in order to improve accuracy and efficiency. We use sets of fuzzy association rules that are mined from network audit data as models of " normal behavior. " To detect anoma...

Neuro-Fuzzy Modeling has been applied in a wide variety of fields such as Decision Making, Engineering and Management Sciences etc. In particular, applications of this Modeling technique in Decision Making by involving complex Systems of Linear Algebraic Equations have remarkable significance. In this Paper, we present Polak-Ribiere Conjugate Gradient based Neural Network with Fuzzy rules to so...

Themain advantage of evolutionary multi-objective optimization (EMO) over classical approaches is that a variety of non-dominated solutions with a wide range of objective values can be simultaneously obtained by a single run of an EMO algorithm. In this chapter, we show how this advantage can be utilized in the design of fuzzy ensemble classifiers. First we explain three objectives in multi-obj...

Applying the concept of fuzzy logic (an abstract version of Boolean logic) to well-known algorithms generates an abstract version (i.e., fuzzy algorithm) that often results in computational improvements. Precision may be reduced but counteracted by gaining computational efficiency. The trade-offs (e.g., small increase in space, loss of precision) for a variety of applications are deemed accepta...

Hybrid intelligent systems combining fuzzy logic and neural networks are proving their effectiveness in a wide variety of real-world problems. Fuzzy logic and neural nets have particular computational properties that make them suited for particular problems and not for others. For example, while neural networks are good at recognizing patterns, they are not good at explaining how they reach the...

Data-driven fuzzy modeling has been used in a wide variety of applications. However, in fuzzy rule-based models acquired from numerical data, redundancy often exists in the form of redundant rules or similar fuzzy sets. This results in unnecessary structural complexity and decreases the interpretability of the system. In this paper, a rule-base self-extraction and simpli&cation method is propos...

This paper addresses some questions about the variety generated by the algebra of truth values of type-2 fuzzy sets. Its principal result is that this variety is generated by a nite algebra, and in particular is locally nite. This provides an algorithm for determining when an equation holds in this variety. It also sheds light on the question of determining an equational axiomatization of this ...