Intrusion detection techniques based on improved intuitionistic fuzzy neural networks

2017 ◽  
Vol 8 (1) ◽  
pp. 41
Author(s):  
Jing Ma ◽  
Weiwei Kong ◽  
Yang Lei
Keyword(s):  
Neural Networks ◽  
Intrusion Detection ◽  
Fuzzy Neural Networks ◽  
Detection Techniques ◽  
Intuitionistic Fuzzy ◽  
Fuzzy Neural

Intrusion Detection Technique Based on Improved Intuitionistic Fuzzy Neural Networks

2015 ◽  
Vol 713-715 ◽  
pp. 2507-2510
Author(s):  
Yang Lei ◽  
Jing Ma
Keyword(s):  
Neural Networks ◽  
Intrusion Detection ◽  
Computer Security ◽  
Detection Method ◽  
Fuzzy Neural Networks ◽  
Detection Methods ◽  
Detection Rates ◽  
Intuitionistic Fuzzy ◽  
Sufficient Stability ◽  
Fuzzy Neural

At present, the issue of intrusion detection has been a hot point to all over the computer security area. In this paper, a novel intrusion detection method has been proposed. Unlike the current existent detection methods, this paper combines the theories of both intuitionistic fuzzy sets (IFS) and artificial neural networks (ANN) together, which leads to much fewer iteration numbers, higher detection rates and sufficient stability. Experimental results show that the now method proposed in this paper is promising and has obvious superiorities over other current typical ones.

Modeling of Self-Induced Vibrations that Occur During the Machining Process of Casting Patterns with the Use of The Fuzzy-Neural Networks Method

2013 ◽  
Vol 58 (3) ◽  
pp. 871-875
Author(s):  
A. Herberg
Keyword(s):  
Neural Networks ◽  
Control System ◽  
Network Structure ◽  
Fuzzy Systems ◽  
Fuzzy Neural Networks ◽  
Cnc Machining ◽  
Machining Process ◽  
Modeling Process ◽  
Fuzzy Neural ◽  
Takagi Sugeno

Abstract This article outlines a methodology of modeling self-induced vibrations that occur in the course of machining of metal objects, i.e. when shaping casting patterns on CNC machining centers. The modeling process presented here is based on an algorithm that makes use of local model fuzzy-neural networks. The algorithm falls back on the advantages of fuzzy systems with Takagi-Sugeno-Kanga (TSK) consequences and neural networks with auxiliary modules that help optimize and shorten the time needed to identify the best possible network structure. The modeling of self-induced vibrations allows analyzing how the vibrations come into being. This in turn makes it possible to develop effective ways of eliminating these vibrations and, ultimately, designing a practical control system that would dispose of the vibrations altogether.

Sign in / Sign up

Export Citation Format

Share Document