NDLI: Exploitation of minimum risk system based on Artificial Neural Network

Content Provider	IEEE Xplore Digital Library
Author	Guan Ping
Copyright Year	2011
Description	Author affiliation: Beihua University, Jilin, China (Guan Ping)
Abstract	The basic function of a neural system is to intelligent learning from specific examples known as neurons. It has great pattern adaptive capability that may be used to judge between old model and well model. Neural systems have many characteristics such as autonomous, uniqueness, recognition of foreigners, noise tolerance, and distributed detection. Inspired by neural network system, Artificial Neural Network has emerged during the last decade. It is incited by many researchers to build, study, and design neural-based models for a variety of application regions. Artificial neural system can be defined as adaptive model that is inspired by neural network, observed neural functions, mechanisms and principles. Association rule mining is one of well researched and the most important techniques of datum mining. The purpose of association rules is to refine interesting correlations, associations, frequent patterns, or casual constructions in sets of aims in other datum repositories or the transaction databases. Association rule is widely used in various regions such as telecommunication network, inventory control, intelligent decision, risk management and market analysis etc. Artificial Neural Network is the most widely used algorithm for mining the association rules. In this paper, Artificial Neural Network is studied and optimized based on classification system. The performance of the ANN based on classification system is evaluated by varying number of generations and computing accuracy at different factors. Three standard datum had been used to computer the accuracy. The test result shows that the system can give highest accuracy more than o.4.
Starting Page	508
Ending Page	511
File Size	94179
Page Count	4
File Format	PDF
ISBN	9781612847016
e-ISBN	9781612847047
DOI	10.1109/ITiME.2011.6132160
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-12-09
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Computers classification system Computational modeling Cloning Artificial neural networks Classification algorithms Neural network association rule mining Algorithm Data mining confidence and support counting
Content Type	Text
Resource Type	Article

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