Koda, Masato (2012) Chaos Search in Fourier Amplitude Sensitivity Test. Journal of Information and Communication Technology, 11. pp. 1-16. ISSN 2180-3862

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Abstract

Work in Artificial Intelligence (AI) often involves search algorithms. In many complicated problems, however, local search algorithms may fail to converge into global optimization and global search procedures are needed. In this paper, we investigate the Fourier Amplitude Sensitivity Test (FAST) as an example of a global sensitivity analysis tool for complex, non-linear dynamical systems. FAST was originally developed based on the Fourier series expansion of a model output and on the assumption that samples of model inputs are uniformly distributed in a high dimensional parameter space. In order to compute sensitivity indices, the parameter space needs to be searched utilizing an appropriate (space-filling) search curve. In FAST, search curves are defined through learning functions, selection of which will heavily affect the global searching capacity and computational efficiency. This paper explores the characterization of learning functions involved in FAST and derives the underlying dynamical relationships with chaos search, which can provide new learning algorithms. This contribution has proven the general link that exists between chaos search and FAST, which helps us exploit the ergodicity of chaos search in AI applications.

Item Type:	Article
Uncontrolled Keywords:	Fourier amplitude sensitivity test (FAST), global sensitivity analysis, chaos search, artificial intelligence (AI)
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	School of Computing
Depositing User:	Mrs Nurin Jazlina Hamid
Date Deposited:	14 Feb 2024 14:59
Last Modified:	14 Feb 2024 14:59
URI:	https://repo.uum.edu.my/id/eprint/30416

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