.png){kind=icon}
返回
Shape recognition based on neural networks trained by differential evolution algorithm
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neucom.2006.10.026.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this paper a new method for recognition of 2D occluded shapes based on neural networks using generalized differential evolution training algorithm is proposed. Firstly, a generalization strategy of differential evolution algorithm is introduced. And this global optimization algorithm is applied to train the multilayer perceptron neural networks. The proposed algorithms are evaluated through a plant species identification task involving 25 plant species. For this practical problem, a multiscale Fourier descriptors (MFDs) method is applied to the plant images to extract shape features. Finally, the experimental results show that our proposed GDE training method is feasible and efficient for large-scale shape recognition problem. Moreover, the experimental results illustrated that the GDE training algorithm combined with gradient-based training algorithms will achieve better convergence performance. (c) 2006 Elsevier B.V. All rights reserved.
Keyword:
shape recognition
generalization strategy
differential evolution algorithm
multiscale Fourier descriptors
leaf image database
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:
6.5
论文数:
2.5W
被引数:
6.5W
机构
暂无机构信息
引用论文
Institutional response to the 2022 iodinated contrast shortage: A narrative review对2022年碘造影剂短缺的机构性应对：一项叙述性综述
PREAI
分享收藏Curvature scale-space-driven object recognition with an indexing scheme based on artificial neural networks.png)
PATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
PREAI
分享收藏分享收藏
分享收藏Preliminary Evaluation of an Injectable Sensor for Subcutaneous Photoplethysmography in Animals
PREAI
分享收藏 没有更多内容
没有更多内容