Please use this identifier to cite or link to this item: http://nopr.niscair.res.in/handle/123456789/6032
Title: Mathematical model and rule extraction for tool wear monitoring problem using nature inspired techniques
Authors: Omkar, S N
Senthilnath, J
Suresh, S
Keywords: Tool wear monitoring
Genetic Programming
Ant-Miner
Issue Date: Aug-2009
Publisher: CSIR
Abstract: In this paper, pattern classification problem in tool wear monitoring is solved using nature inspired techniques such as Genetic Programming (GP) and Ant-Miner (AM). The main advantage of GP and AM is their ability to learn the underlying data relationships and express them in the form of mathematical equation or simple rules. The extraction of knowledge from the training data set using GP and AM are in the form of Genetic Programming Classifier Expression (GPCE) and rules respectively. The GPCE and AM extracted rules are then applied to set of data in the testing/validation set to obtain the classification accuracy. A major attraction in GP evolved GPCE and AM based classification is the possibility of obtaining an expert system like rules that can be directly applied subsequently by the user in his/her application. The performance of the data classification using GP and AM is as good as the classification accuracy obtained in the earlier study (i.e. using ANN approach).
Description: 205-210
URI: http://hdl.handle.net/123456789/6032
ISSN: 0975-1017 (Online); 0971-4588 (Print)
Appears in Collections:IJEMS Vol.16(4) [August 2009]

Files in This Item:
File Description SizeFormat 
IJEMS 16(4) 205-210.pdf87.05 kBAdobe PDFView/Open


Items in NOPR are protected by copyright, with all rights reserved, unless otherwise indicated.