Speedup of Interval Type 2 Fuzzy Logic Systems Based on GPU for Robot Navigation | Zendy

Long Thanh Ngo | Zendy; Dzung Dinh Nguyen | Zendy; Long The Pham | Zendy; Cuong Manh Luong | Zendy

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Speedup of Interval Type 2 Fuzzy Logic Systems Based on GPU for Robot Navigation

Author(s) -

Long Thanh Ngo,

Dzung Dinh Nguyen,

Long The Pham,

Cuong Manh Luong

Publication year - 2012

Publication title -

advances in fuzzy systems

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.38

H-Index - 19

eISSN - 1687-711X

pISSN - 1687-7101

DOI - 10.1155/2012/698062

Subject(s) - speedup , computer science , graphics processing unit , fuzzy logic , parallel computing , central processing unit , cuda , interval (graph theory) , computation , graphics , general purpose computing on graphics processing units , algorithm , computational science , artificial intelligence , computer graphics (images) , mathematics , computer hardware , combinatorics

As the number of rules and sample rate for type 2 fuzzy logic systems (T2FLSs) increases, the speed of calculations becomes a problem. The T2FLS has a large membership value of inherent algorithmic parallelism that modern CPU architectures do not exploit. In the T2FLS, many rules and algorithms can be speedup on a graphics processing unit (GPU) as long as the majority of computation a various stages and components are not dependent on each other. This paper demonstrates how to install interval type 2 fuzzy logic systems (IT2-FLSs) on the GPU and experiments for obstacle avoidance behavior of robot navigation. GPU-based calculations are high-performance solution and free up the CPU. The experimental results show that the performance of the GPU is many times faster than CPU

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