Fuzzy whale optimisation algorithm: a new hybrid approach for automatic sonar target recognition

IF 1.7 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Journal of Experimental & Theoretical Artificial Intelligence Pub Date : 2022-02-13 DOI:10.1080/0952813X.2021.1960639
A. Saffari, S. Zahiri, M. Khishe
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引用次数: 6

Abstract

ABSTRACT In this paper, a radial basis function neural network (RBF-NN) automatic sonar target recognition system is proposed. For the RBF-NN training phase, a whale optimisation algorithm (WOA) developed with a fuzzy system has been used (which is called FWOA). The reason for using the fuzzy system is the lack of correct identification of the boundary between the two stages of exploration and exploitation. Thus, the tuning of the effective parameters of the WOA is left to the fuzzy system of the Mamdani type. RBF-NN was trained by chimp optimisation algorithm (ChOA), genetic algorithm (GA), Evolution Strategy (ES), league championship algorithm (LCA), grey wolf algorithms (GWO), gravitational search algorithm (GSA), and WOA to compare the proposed algorithm. The measured criteria are convergence speed, ability to avoid local optimisation, and classification rate. The simulation results showed that FWOA with 97.49% classification accuracy rate in sonar data performed better than the other seven benchmark algorithms.
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模糊鲸优化算法:一种新的混合方法用于自动声纳目标识别
提出了一种径向基函数神经网络(RBF-NN)自动声纳目标识别系统。在RBF-NN的训练阶段,使用了一种基于模糊系统的鲸鱼优化算法(whale optimization algorithm, WOA)(称为FWOA)。使用模糊系统的原因是缺乏对勘探和开发两个阶段边界的正确识别。因此,WOA有效参数的调整留给了Mamdani型模糊系统。采用黑猩猩优化算法(ChOA)、遗传算法(GA)、进化策略(ES)、联赛冠军算法(LCA)、灰狼算法(GWO)、引力搜索算法(GSA)和WOA对RBF-NN进行训练,比较所提算法。测量的标准是收敛速度、避免局部优化的能力和分类率。仿真结果表明,FWOA在声纳数据中的分类准确率为97.49%,优于其他7种基准算法。
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来源期刊
CiteScore
6.10
自引率
4.50%
发文量
89
审稿时长
>12 weeks
期刊介绍: Journal of Experimental & Theoretical Artificial Intelligence (JETAI) is a world leading journal dedicated to publishing high quality, rigorously reviewed, original papers in artificial intelligence (AI) research. The journal features work in all subfields of AI research and accepts both theoretical and applied research. Topics covered include, but are not limited to, the following: • cognitive science • games • learning • knowledge representation • memory and neural system modelling • perception • problem-solving
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