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浏览/检索结果: 共4条，第1-4条 帮助 限定条件 专题：长春光学精密机械与物理研究所    第一署名单位    第一作者单位    通讯作者单位     已选(0)清除 条数/页：5101520253035404550556065707580859095100   排序方式：请选择作者升序作者降序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 Ensemble learning based on policy optimization neural networks for capability assessment 期刊论文 Sensors, 2021, 卷号: 21, 期号: 17 作者:  F. Zhang;  J. Li;  Y. Wang;  L. Guo;  D. Wu 收藏  |  浏览/下载：3/0  |  提交时间：2022/06/13 Predicting tool wear with multi-sensor data using deep belief networks 期刊论文 International Journal of Advanced Manufacturing Technology, 2018, 卷号: 99, 期号: 2019-05-08, 页码: 1917-1926 作者:  Chen, Y. X.;  Jin, Y.;  Jiri, G. 收藏  |  浏览/下载：2/0  |  提交时间：2019/09/17 Tool wear prediction  Deep belief network  Support vector regression  Artificial neural network  neural-networks  diagnosis  state  model  optimization  prognostics  machinery  algorithm  filter  Automation & Control Systems  Engineering   The registration of aerial infrared and visible images (EI CONFERENCE) 会议论文 2010 International Conference on Educational and Information Technology, ICEIT 2010, September 17, 2010 - September 19, 2010, Chongqing, China Sun M.; Bao Z.; Liu J.; Wang Y.; Quan Y. 收藏  |  浏览/下载：14/0  |  提交时间：2013/03/25 In order to solve the registration problem of different source image existed on aerial image fusion  algorithms based on Particle Swarm Optimization (PSO) are applied as search strategy in this paper  and Alignment Metric (AM) is used as judgment. This study has realized the different source image registration of infrared and visible light with high speed  high accuracy and high reliability. Basically  with little restriction of gray level properties  a new alignment measure is applied  which can efficiently measure the image registration extent and tolerate noise well. Even more  the intelligent optimization algorithm - Particle Swarm Optimization (PSO) is combined to improve the registration precision and rate of infrared and visible light. Experimental results indicate that  the study attains the registration accuracy of pixel level  and every registration time is cut down over 40 percent compared to traditional method. The match algorithm based on AM  solves the registration problem that greater differences between different source images are existed on gray and characteristic. At the same time  the adoption of combining the intelligent optimization algorithms significantly improves the searching efficiency and convergence speed of the algorithms  and the registration result has higher accuracy and stability  which builds up solid foundation for different source image fusion. The method in this paper has a magnificent effect  and is easy for application and very suitable for engineering use. 2010 IEEE.   An improved discrete particle swarm optimization algorithm for TSP (EI CONFERENCE) 会议论文 2007 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Workshops, WI-IAT Workshops 2007, November 2, 2007 - November 5, 2007, Silicon Valley, CA, United states Zhang C.; Sun J.; Wang Y.; Yang Q. 收藏  |  浏览/下载：15/0  |  提交时间：2013/03/25 An Improved discrete particle swarm optimization (DPSO)-based algorithm for the traveling salesman problem (TSP) is proposed. In order to overcome the problem of premature convergence  a novel depressor is proposed and a diversity measure to control the swarm is also introduced which can be used to switch between the attractor and depressor. The proposed algorithm has been applied to a set of benchmark problems and compared with the existing algorithms for solving TSP using swarm intelligence. The results show that it can prevent premature convergence to a high degree  but still keeps a rapid convergence like the basic DPSO. 2007 IEEE.