NDLI: Evolutionary generation and training of recurrent artificial neural networks

Content Provider	IEEE Xplore Digital Library
Author	Santos, J. Duro, R.J.
Copyright Year	1994
Description	Author affiliation: Departimento de Ingenieria Ind., Univ. de La Coruna, Spain (Santos, J.; Duro, R.J.)
Abstract	An evolutionary artificial neural network training and design methodology is presented, aimed at obtaining optimum or quasi-optimum synchronous recurrent neural networks capable of processing sequential inputs. We show that, through the use of this method and working with floating point and integer valued chromosomes, it is possible to achieve optimum results, considering very small populations and few generations. In order to implement this methodology, we have developed GENIAL, a genetic algorithm development environment which is specifically designed for solving this type of problem. It offers ways of testing adequate fitness functions and many tools for improving results. Finally, we comment on the sequential introduction of different constraints in genetic algorithms, presenting a classical example where several design requirements are met simultaneously and which demonstrates the power of this method.<>
Starting Page	759
Ending Page	763
File Size	420180
Page Count	5
File Format	PDF
ISBN	0780318994
DOI	10.1109/ICEC.1994.349960
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1994-06-27
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Artificial neural networks Genetic algorithms Network topology Neurons Industrial training Algorithm design and analysis Feedback loop Electronic mail Design methodology Recurrent neural networks
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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