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Parallelizing fuzzy rule generation using GPGPU

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Abstract

This article proposes a method to parallelize the process of generating fuzzy if-then rules for pattern classification problems in order to reduce the computational time. The proposed method makes use of general purpose computation on graphics processing units (GPGPUs)’ parallel implementation with compute unified device architecture (CUDA), a development environment. CUDA contains a library to perform matrix operations in parallel. In the proposed method, published source codes of matrix multiplication are modified so that the membership values of given training patterns with antecedent fuzzy sets are calculated. In a series of computational experiments, it is shown that the computational time is reduced for those problems that require high computational effort.

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References

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Correspondence to Tomoharu Nakashima.

Additional information

This work was presented in part at the 16th International Symposium on Artificial Life and Robotics, Oita, Japan, January 27–29, 2011

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Uenishi, T., Nakashima, T. & Fujimoto, N. Parallelizing fuzzy rule generation using GPGPU. Artif Life Robotics 16, 214–218 (2011). https://doi.org/10.1007/s10015-011-0920-1

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  • DOI: https://doi.org/10.1007/s10015-011-0920-1

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