Abstract
To make interval-valued granular reasoning efficiently and optimize interval membership functions based on training data effectively, a new Genetic Granular Neural Network (GGNN) is desinged. Simulation results have shown that the GGNN is able to extract useful fuzzy knowledge effectively and efficiently from training data to have high training accuracy.
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Zhang, YQ., Jin, B., Tang, Y. (2007). Genetic Granular Neural Networks. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4492. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72393-6_61
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DOI: https://doi.org/10.1007/978-3-540-72393-6_61
Publisher Name: Springer, Berlin, Heidelberg
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