Please use this identifier to cite or link to this item: http://hdl.handle.net/11452/22281
Title: Hybrid neural network and genetic algorithm based machining feature recognition
Authors: Uludağ Üniversitesi/Mühendislik Fakültesi/Endüstri Mühendisliği Bölümü.
Uludağ Üniversitesi/Mühendislik Fakültesi/Makine Mühendisliği Bölümü.
Öztürk, Nursel
Öztürk, Ferruh
AAG-9336-2021
AAG-9923-2021
7005688805
56271685800
Keywords: Computer science
Engineering
Feature recognition
Neural networks
Genetic input selection
Manufacturing features
Design
Classification
System
Search
Model
Backpropagation
Computational complexity
Computer aided manufacturing
Feature extraction
Genetic algorithms
Image processing
Machining
Mathematical models
Parameter estimation
Problem solving
Computer aided production systems
Feature recognition
Genetic input selection
Network model
Neural networks
Issue Date: Jun-2004
Publisher: Springer
Citation: Öztürk, N. ve Öztürk, F. (2004). “Hybrid neural network and genetic algorithm based machining feature recognition”. Journal of Intelligent Manufacturing, 15(3), 287-298.
Abstract: In this research, neural networks (NNs) and genetic algorithms (GAs) are used together in a hybrid approach to reduce the computational complexity of feature recognition problem. The proposed approach combines the characteristics of evolutionary technique and NN to overcome the shortcomings of feature recognition problem. Consideration is given to reduce the computational complexity of network with specific interest to design the optimum network architecture using GA input selection approach. In order to evaluate the performance of the proposed system, experimental results are compared with previous NN based feature recognition research.
URI: https://doi.org/10.1023/B:JIMS.0000026567.63397.d5
https://link.springer.com/article/10.1023/B:JIMS.0000026567.63397.d5
http://hdl.handle.net/11452/22281
ISSN: 0956-5515
Appears in Collections:Scopus
Web of Science

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