- Tytuł:
- Hardware implementation of a Takagi-Sugeno neuro-fuzzy system optimized by a population algorithm
- Autorzy:
-
Dziwiński, Piotr
Przybył, Andrzej
Trippner, Paweł
Paszkowski, Józef
Hayashi, Yoichi - Powiązania:
- https://bibliotekanauki.pl/articles/2031120.pdf
- Data publikacji:
- 2021
- Wydawca:
- Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
- Tematy:
-
hardware implementation of fuzzy systems
FPGA
population algorithm - Opis:
- Over the last several decades, neuro-fuzzy systems (NFS) have been widely analyzed and described in the literature because of their many advantages. They can model the uncertainty characteristic of human reasoning and the possibility of a universal approximation. These properties allow, for example, for the implementation of nonlinear control and modeling systems of better quality than would be possible with the use of classical methods. However, according to the authors, the number of NFS applications deployed so far is not large enough. This is because the implementation of NFS on typical digital platforms, such as, for example, microcontrollers, has not led to sufficiently high performance. On the other hand, the world literature describes many cases of NFS hardware implementation in programmable gate arrays (FPGAs) offering sufficiently high performance. Unfortunately, the complexity and cost of such systems were so high that the solutions were not very successful. This paper proposes a method of the hardware implementation of MRBF-TS systems. Such systems are created by modifying a subclass of Takagi-Sugeno (TS) fuzzy-neural structures, i.e. the NFS group functionally equivalent to networks with radial basis functions (RBF). The structure of the MRBF-TS is designed to be well suited to the implementation on an FPGA. Thanks to this, it is possible to obtain both very high computing efficiency and high accuracy with relatively low consumption of hardware resources. This paper describes both, the method of implementing MRBFTS type structures on the FPGA and the method of designing such structures based on the population algorithm. The described solution allows for the implementation of control or modeling systems, the implementation of which was impossible so far due to technical or economic reasons.
- Źródło:
-
Journal of Artificial Intelligence and Soft Computing Research; 2021, 11, 3; 243-266
2083-2567
2449-6499 - Pojawia się w:
- Journal of Artificial Intelligence and Soft Computing Research
- Dostawca treści:
- Biblioteka Nauki
Artykuł