%0 Conference Paper %1 Fukumi1996 %A Fukumi, Minoru %A Akamatsu, Norio %B Systems, Man, and Cybernetics, 1996. IEEE International Conference on %D 1996 %I IEEE Computer Society %K (artificial Fourier algorithm, algorithms, coin convergence, deterministic features fitness, genetic intelligence), invariant learning learning, multilayer multilayered mutation, network network, neural partial pattern perceptrons, problem, recognition recognitionFourier rotated rotation spectra, system, transform, transforms, %P 1989--1993 %R 10.1109/ICSMC.1996.565432 %T A method to design a neural pattern recognition system by using a genetic algorithm with partial fitness and a deterministic mutation %U http://ieeexplore.ieee.org/iel3/4232/12283/00565432.pdf?tp=&arnumber=565432&isnumber=12283 %V 3 %X This paper presents a method using a genetic algorithm (GA) with a partial fitness (PF) and a deterministic mutation (DM) to design a neural pattern recognition system for a rotated coin recognition problem. In the method, chromosomes in the GA are divided into several parts. Their PFs are evaluated for GA operations. Furthermore, this paper introduces the DM based on a neural network learning. A coin recognition system in this paper includes as a preprocessor the Fourier transform, which produces rotation invariant features. Those features are recognized by a multilayered neural network. The GA is utilized to reduce the number of input signals, Fourier spectra, into the neural network. It is shown that the present method is better than conventional GAs on convergence in learning and makes a small-sized neural network

@inproceedings{Fukumi1996, abstract = {This paper presents a method using a genetic algorithm (GA) with a partial fitness (PF) and a deterministic mutation (DM) to design a neural pattern recognition system for a rotated coin recognition problem. In the method, chromosomes in the GA are divided into several parts. Their PFs are evaluated for GA operations. Furthermore, this paper introduces the DM based on a neural network learning. A coin recognition system in this paper includes as a preprocessor the Fourier transform, which produces rotation invariant features. Those features are recognized by a multilayered neural network. The GA is utilized to reduce the number of input signals, Fourier spectra, into the neural network. It is shown that the present method is better than conventional GAs on convergence in learning and makes a small-sized neural network }, 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 Akamatsu, Norio}, biburl = {https://www.bibsonomy.org/bibtex/278a755863d7e81fdec33836c03f6036c/cocus}, booktitle = {Systems, Man, and Cybernetics, 1996. IEEE International Conference on}, booktitleaddon = {October 14--17, 1996}, doi = {10.1109/ICSMC.1996.565432}, file = {:./fukumi1996_00565432-1.pdf:PDF}, interhash = {ec7f0941600f56187934031305e155c0}, intrahash = {78a755863d7e81fdec33836c03f6036c}, keywords = {(artificial Fourier algorithm, algorithms, coin convergence, deterministic features fitness, genetic intelligence), invariant learning learning, multilayer multilayered mutation, network network, neural partial pattern perceptrons, problem, recognition recognitionFourier rotated rotation spectra, system, transform, transforms,}, location = {#ieeeaddr#}, owner = {CK}, pages = {1989--1993}, publisher = {{IEEE} Computer Society}, timestamp = {2011-03-27T19:35:39.000+0200}, title = {A method to design a neural pattern recognition system by using a genetic algorithm with partial fitness and a deterministic mutation}, titleaddon = {Octpber 14--17, 1996}, url = {http://ieeexplore.ieee.org/iel3/4232/12283/00565432.pdf?tp=&arnumber=565432&isnumber=12283}, urldate = {25-1-2008}, venue = {Beijing, China}, volume = 3, volumes = {4}, xcrossref = {CK:conf/ieeesmc/1996}, year = 1996 }