机构

• 长春光学精密机械与物... [2]
• 紫金山天文台 [1]

采集方式

• OAI收割 [3]

内容类型

• 会议论文 [2]
• 期刊论文 [1]

发表日期

• 2018 [1]
• 2009 [1]
• 2008 [1]

学科主题

筛选

浏览/检索结果: 共3条，第1-3条 帮助 条数/页： 5101520253035404550556065707580859095100 排序方式： 请选择题名升序题名降序发表日期升序发表日期降序提交时间升序提交时间降序作者升序作者降序 Black Soil Organic Matter Content Estimation Using Hybrid Selection Method Based on RF and GABPSO 期刊论文  OAI收割 SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 卷号: 38, 期号: 1, 页码: 181-187 作者:  Ma Yue;  Jiang Qi-gang;  Meng Zhi-guo;  Liu Hua-xin   |  收藏  |  浏览/下载：32/0  |  提交时间：2019/04/08 Hyperspectral  Black soil organic matter content  Genetic algorithm  Binary particle swarm optimization  Random forest   Using bidirectional binary particle swarm optimization for feature selection in feature-level fusion recognition system (EI CONFERENCE) 会议论文  OAI收割 2009 4th IEEE Conference on Industrial Electronics and Applications, ICIEA 2009, May 25, 2009 - May 27, 2009, Xi'an, China 作者:  Wang D.;  Wang Y.;  Wang Y.;  Wang Y.;  Wang Y. 收藏  |  浏览/下载：30/0  |  提交时间：2013/03/25 In feature-level fusion recognition system  the other is optimizing system sensor design to get outstanding cost performance. So feature selection become usually necessary to reduce dimensionality of the combination of multi-sensor features and improve system performance in system design. In general  there are two main missions. One is improving the recognition correct rate as soon as possible  the optimization is usually applied to feature selection because of its computational feasibility and validity. For further improving recognition accuracy and reducing selected feature dimensions  this paper presents a more rational and accurate optimization  Bidirectional Binary Particle Swarm Optimization (BBPSO) algorithm for feature selection in feature-level fusion target recognition system. In addition  we introduce a new evaluating function as criterion function in BBPSO feature selection method. At the last  we utilized Leave-One-Out method to validate the proposed method. The experiment results show that the proposed algorithm improves classification accuracy by two percentage points  while the selected feature dimensions are less one dimension than original Particle Swarm Optimization approach with 16 original feature dimensions. 2009 IEEE.   A comparative study of discrete differential evolution on binary constraint satisfaction problems (EI CONFERENCE) 会议论文  OAI收割 2008 IEEE Congress on Evolutionary Computation, CEC 2008, June 1, 2008 - June 6, 2008, Hong Kong, China Yang Q. 收藏  |  浏览/下载：73/0  |  提交时间：2013/03/25 There are some variants and applications of the discretization of differential evolution. Performances of discrete differential evolution algorithms on random binary constraint satisfaction problem are studied in this paper  and a novel discrete differential evolution algorithm based on exchanging elements is proposed. We compare the proposed discrete differential evolution  evolutionary algorithms and discrete particle swarm optimization on random binary constraint satisfaction problems. Experimental results indicate though the proposed algorithm is simpler  it is competitive with other evolutionary algorithms solving constraint satisfaction problems. 2008 IEEE.