基于群体算法优化的Takagi-Sugeno神经模糊系统的硬件实现

IF 3.3 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Journal of Artificial Intelligence and Soft Computing Research Pub Date : 2021-05-29 DOI:10.2478/jaiscr-2021-0015
P. Dziwiński, A. Przybył, P. Trippner, J. Paszkowski, Y. Hayashi
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引用次数: 6

摘要

摘要在过去的几十年里,神经模糊系统因其许多优点而在文献中得到了广泛的分析和描述。它们可以模拟人类推理的不确定性特征和普遍近似的可能性。例如,这些特性允许实现比使用经典方法更好质量的非线性控制和建模系统。然而,根据作者的说法,到目前为止部署的NFS应用程序数量还不够多。这是因为在典型的数字平台(例如微控制器)上实现NFS并没有带来足够高的性能。另一方面,世界文献描述了在提供足够高性能的可编程门阵列(FPGA)中实现NFS硬件的许多情况。不幸的是,这种系统的复杂性和成本太高,以至于解决方案并不十分成功。本文提出了一种MRBF-TS系统的硬件实现方法。这样的系统是通过修改Takagi-Sugeno(TS)模糊神经结构的一个子类来创建的,即在功能上等效于具有径向基函数(RBF)的网络的NFS组。MRBF-TS的结构被设计为非常适合在FPGA上实现。得益于此,可以以相对低的硬件资源消耗获得非常高的计算效率和高精度。本文介绍了在FPGA上实现MRBFTS型结构的方法,以及基于总体算法设计这种结构的方法。所描述的解决方案允许实现控制或建模系统,由于技术或经济原因,迄今为止不可能实现这些系统。
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Hardware Implementation of a Takagi-Sugeno Neuro-Fuzzy System Optimized by a Population Algorithm
Abstract 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.
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来源期刊
Journal of Artificial Intelligence and Soft Computing Research
Journal of Artificial Intelligence and Soft Computing Research COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
7.00
自引率
25.00%
发文量
10
审稿时长
24 weeks
期刊介绍: Journal of Artificial Intelligence and Soft Computing Research (available also at Sciendo (De Gruyter)) is a dynamically developing international journal focused on the latest scientific results and methods constituting traditional artificial intelligence methods and soft computing techniques. Our goal is to bring together scientists representing both approaches and various research communities.
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