Abstract
The present paper proposes the automatic design of Feed-Forward Spiking Neural Networks by representing several inherent aspects of the neural architecture in a proposed Context-Free Grammar; which is evolved through an Evolutionary Strategy. In the indirect design, the power of the design and the capabilities of the designed neural network are strongly related with the complexity of the grammars. The neural networks designed with the proposed grammar are tested with two well-known benchmark datasets of pattern recognition. Finally, neural networks derived from the proposed grammar are compared with other generated by similar grammars which were designed for the same purposed, the neural network design.
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Acknowledgments
The authors thank to Consejo Nacional de Ciencia y Tecnología (CONACyT) and Instituto Tecnológic de México - Instituto Tecnológico de León for the support to this research.
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Espinal, A., Carpio, M., Ornelas, M., Puga, H., Melín, P., Sotelo-Figueroa, M. (2015). Evolutionary Indirect Design of Feed-Forward Spiking Neural Networks. In: Melin, P., Castillo, O., Kacprzyk, J. (eds) Design of Intelligent Systems Based on Fuzzy Logic, Neural Networks and Nature-Inspired Optimization. Studies in Computational Intelligence, vol 601. Springer, Cham. https://doi.org/10.1007/978-3-319-17747-2_7
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DOI: https://doi.org/10.1007/978-3-319-17747-2_7
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