%0 Conference Paper %1 Fukumi1993 %A Fukumi, Minoru %A Omatu, Sigeru %B Proceedings of the IEEE International joint Conference on Neural Networks 1993, IJCNN'93 %D 1993 %I IEEE Computer Society %K algorithm, algorithms, architecture architecture, back-propagation, coin design, genetic net network neural object recognition rotated variably %P 2109--2112 %R 10.1109/IJCNN.1993.714140 %T Designing a neural network for coin recognition by a genetic algorithm %U http://ieeexplore.ieee.org/iel4/5797/15470/00714140.pdf?tp=&arnumber=714140&isnumber=15470 %V 3 %X This paper presents a method to design a neural network for coin recognition by a genetic algorithm (GA). The GA specifies an architecture of neural network, but does not train the network. The back-propagation (BP) method trains the network. After training it by the BP, the GA varies the architecture of the network to fit the environment, which is to achieve a 100\% recognition accuracy and to make the network small in size. The network reduced by the GA is further decreased by using the BP with forgetting of weight. The object of this paper is to design a smaller neural network for hardware implementation of coin recognition system. Results by computer simulation show the effectiveness of the method to variably rotated coin recognition problem.

@inproceedings{Fukumi1993, abstract = { This paper presents a method to design a neural network for coin recognition by a genetic algorithm (GA). The GA specifies an architecture of neural network, but does not train the network. The back-propagation (BP) method trains the network. After training it by the BP, the GA varies the architecture of the network to fit the environment, which is to achieve a 100\% recognition accuracy and to make the network small in size. The network reduced by the GA is further decreased by using the BP with forgetting of weight. The object of this paper is to design a smaller neural network for hardware implementation of coin recognition system. Results by computer simulation show the effectiveness of the method to variably rotated coin recognition problem.}, added-at = {2011-03-27T19:35:34.000+0200}, affiliation = {University of Tokushima, Faculty of Engineering, Department of Information Science and Intelligent Systems}, author = {Fukumi, Minoru and Omatu, Sigeru}, biburl = {https://www.bibsonomy.org/bibtex/21fa9c352c405c10214f4eeca17ad96b2/cocus}, booktitle = {Proceedings of the IEEE International joint Conference on Neural Networks 1993, IJCNN'93}, booktitleaddon = {October 25--29, 1993}, doi = {10.1109/IJCNN.1993.714140}, file = {:./fukumi1993_00714140.pdf:PDF}, interhash = {7b2ea6e7bd15996569fffcfa93343c0a}, intrahash = {1fa9c352c405c10214f4eeca17ad96b2}, keywords = {algorithm, algorithms, architecture architecture, back-propagation, coin design, genetic net network neural object recognition rotated variably}, location = {#ieeeaddr#}, owner = {CK}, pages = {2109--2112}, publisher = {{IEEE} Computer Society}, timestamp = {2011-03-27T19:35:39.000+0200}, title = {Designing a neural network for coin recognition by a genetic algorithm}, titleaddon = {\noop{}}, url = {http://ieeexplore.ieee.org/iel4/5797/15470/00714140.pdf?tp=&arnumber=714140&isnumber=15470}, urldate = {25-1-2008}, venue = {Nagoya, Japan}, volume = 3, volumes = {3}, xcrossref = {CK:conf/ijcnn/1993}, year = 1993 }