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- W2801306514 abstract "Backpropagation (BP) algorithms are widely used for propagating adaptive learning to an artificial neural network (ANN), which uses a gradient descent learning approach to train an ANN. But choosing an efficient and appropriate training methodology is still a challenge. The gravitational search algorithm (GSA) is a recently developed metaheuristic approach which tries to find out a global solution by balancing both exploration and exploitation. Though GSA always is guaranteed to provide a better solution, some time it takes more time to generate a solution. In contrary to this, gradient descent-based algorithms have the limitation of getting stuck into local optima. In view of this, the proposed work has tried to do an amalgamation of both the techniques to avoid the individual deficiencies lying in each of the techniques. A synergy of both the techniques is validated by some of the benchmark datasets along with using some benchmark functions. The performance is measured and compared by running the algorithms individually and by the hybrid approach. The generated results establish the validity of the proposed model." @default.
- W2801306514 created "2018-05-17" @default.
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- W2801306514 date "2018-01-01" @default.
- W2801306514 modified "2023-09-27" @default.
- W2801306514 title "An Improvised Backpropagation Neural Network Model Based on Gravitational Search Algorithm for Multinomial Classification" @default.
- W2801306514 cites W1603920668 @default.
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- W2801306514 doi "https://doi.org/10.1007/978-981-10-6890-4_17" @default.
- W2801306514 hasPublicationYear "2018" @default.
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