.png){kind=icon}
返回
A novel approach to design optimal 2-D digital differentiator using vortex search optimization algorithm
.png){kind=icon}
PRE
AI
DOI：10.1007/s11042-020-10012-6.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The designing of 2-D digital differentiator is multimodal and high dimensional problem which requires large number of differentiator coefficients to be optimized, hence conventional design techniques will not lead to optimal 2-D differentiator design. Metaheuristic approaches is a good approach to handle multimodal and high dimensional problem under consideration. In this paper, a novel method for the design of FIR two-dimensional (2-D) digital differentiator with quadrantally odd symmetric properties is proposed. The coefficients of 2-D digital differentiator are computed and optimized using vortex search optimization (VSO) algorithm with the support of L(1)error objective function. The unique combination of VSO and L(1)error fitness function aids in achieving flatter magnitude response in designing of 2-D differentiator. Further, some comparative analysis with the well-known established techniques like cuckoo search algorithm (CSA), particle swarm optimization (PSO) and real coded genetic algorithm (RCGA) methodology is reported over full frequency bands, in order to demonstrate the advantage of the proposed novel 2-D digital differentiator design approach. To measure the performance of proposed system, some parameters are selected for illustration such as absolute magnitude error, mean absolute error, standard deviation and execution time. The excellence of the proposed approach is observed in approximating the ideal response of 2-D differentiator in contrast to other algorithms and to provide global optimal solution.
Keyword:
Digital FIR 2-D differentiator
Vortex search algorithm
Optimization
Meta-heuristic algorithm
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3
论文数:
2.0W
被引数:
3.2W
机构
引用论文
A support vector regression model hybridized with chaotic krill herd algorithm and empirical mode decomposition for regression task基于混沌磷虾群算法和经验模式分解的支持向量回归模型的回归任务.png)
NEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
分享收藏Adaptive infinite impulse response system identification using teacher learner based optimization algorithmAPPLIED INTELLIGENCE
APPLIED INTELLIGENCEIF3.5
PREAI
分享收藏
Electric load forecasting by complete ensemble empirical mode decomposition adaptive noise and support vector regression with quantum-based dragonfly algorithm完全集合经验模态分解自适应噪声和基于量子蜻蜓算法的支持向量回归的电力负荷预测
PREAI
分享收藏Cyclic electric load forecasting by seasonal SVR with chaotic genetic algorithm基于混沌遗传算法的季节性SVR周期性电力负荷预测
PREAI
分享收藏
查看更多内容