Hybrid neural network and genetic algorithm based machining feature recognition
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Date
2004-06
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Springer
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.
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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
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.