Fuzzy sets, uncertainty, and information
Fuzzy sets, uncertainty, and information
Selection of optimal set of weights in a layered network using genetic algorithms
Information Sciences—Intelligent Systems: An International Journal
Use of fuzziness measures in layered networks for object extraction: a generalization
Fuzzy Sets and Systems
An introduction to genetic algorithms
An introduction to genetic algorithms
Novel classification and segmentation techniques with application to remotely sensed images
Transactions on rough sets VII
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Earlier attempts are made to design Hopfield type neural network architecture for object extraction using Genetic Algorithms (GAs). Energy value of the neural network was taken as the index of fitness of the GA. In the present article fuzzy logic reasoning is incorporated into this Neuro-GA hybrid framework to remove some of the drawbacks of earlier attempts. Here, GAs have been used to evolve Hopfield type optimum neural network architecture for object background classification. Each chromosome of the GA represents an architecture. The output status of the neurons at the converged state of the network is viewed as a fuzzy set and measure of fuzziness of this set is taken as a measure of fitness of the chromosome. The best chromosome of the final generation represents the optimum network configuration. When the input images are less noisy, the evolved networks are found to have less (compared to the corresponding energy based objective evaluation) connectivity for providing comparable outputs.