中国科学院机构知识库网格系统: 检索

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	Bio-Inspired Optimization Algorithm Associated with Reinforcement Learning for Multi-Objective Operating Planning in Radioactive Environment 期刊论文 OAI收割 BIOMIMETICS, 2024, 卷号: 9, 期号: 7, 页码: 17 作者: Kong, Shihan; Wu, Fang; Liu, Hao; Zhang, Wei; Sun, Jinan \| 收藏 \| 浏览/下载：14/0 \| 提交时间：2024/09/09 reinforcement learning improved genetic algorithm radioactive environment planning bio-inspired optimization algorithm combinatorial algorithm
	A Multilevel Recognition Model of Water Inrush Sources: A Case Study of the Zhaogezhuang Mining Area 期刊论文 OAI收割 MINE WATER AND THE ENVIRONMENT, 2021, 页码: 10 作者: Lin, Gang; Jiang, Dong; Dong, Donglin; Fu, Jingying; Li, Xiang \| 收藏 \| 浏览/下载：45/0 \| 提交时间：2021/08/19 Water inrush source identification Hydrochemical analysis Improved genetic algorithm Extreme learning machine Zhaogezhuang mine
	A Multilevel Recognition Model of Water Inrush Sources: A Case Study of the Zhaogezhuang Mining Area 期刊论文 OAI收割 MINE WATER AND THE ENVIRONMENT, 2021, 页码: 10 作者: Lin, Gang; Jiang, Dong; Dong, Donglin; Fu, Jingying; Li, Xiang \| 收藏 \| 浏览/下载：20/0 \| 提交时间：2021/08/19 Water inrush source identification Hydrochemical analysis Improved genetic algorithm Extreme learning machine Zhaogezhuang mine
	An Improved Compact Genetic Algorithm for Scheduling Problems in a Flexible Flow Shop with a Multi-Queue Buffer 期刊论文 OAI收割 PROCESSES, 2019, 卷号: 7, 期号: 5, 页码: 1-24 作者: Zhang Q(张权); Han ZH(韩忠华); Zhang, Jingyuan; Shi HB(史海波) \| 收藏 \| 浏览/下载：49/0 \| 提交时间：2019/06/29 flexible flow shop scheduling multi-queue limited buffers improved compact genetic algorithm probability density function of the Gaussian distribution
	Event-driven dynamic job shop scheduling execution based on improved Genetic Algorithm and Ontology 会议论文 OAI收割 2017 Chinese Automation Congress (CAC2017) & China Intelligent Manufacturing International Conference (CIMIC2017), Jinan, China, October 20-22, 2017 作者: Cheng HB(程海波); Xue LL(薛玲玲); Wang P(王鹏); Zeng P(曾鹏); Yu HB(于海斌) \| 收藏 \| 浏览/下载：34/0 \| 提交时间：2017/12/21 dynamic job shop scheduling problems improved genetic algorithm ontology module
	Combination kernel function least squares support vector machine for chaotic time series prediction 期刊论文 OAI收割 ACTA PHYSICA SINICA, 2014, 卷号: 63, 期号: 16 作者: Tian ZhongDa; Gao XianWen; Shi Tong \| 收藏 \| 浏览/下载：9/0 \| 提交时间：2021/02/02 chaotic time series least squares support vector machine combination kernel function improved genetic algorithm
	Integrated optimization method for coupling parametrization design of complex space system (EI CONFERENCE) 会议论文 OAI收割 2012 3rd International Conference on System Science, Engineering Design and Manufacturing Informatization, ICSEM 2012, October 20, 2012 - October 21, 2012, Chengdu, China 作者: Han C.-S.; Wen M. 收藏 \| 浏览/下载：137/0 \| 提交时间：2013/03/25 This paper investigates the problem of optimization method for coupling parametrization design of complex space system. The objective is to develop an integrated optimization framework to design complex space system. An improved collaborative optimization (ICO) strategy for complex design using hybrid optimization algorithm is proposed which associates genetic algorithm (GA) with tabu search (TS). A multi-objective function with penalty terms for the systemlevel optimization and a relax factor for the subsystem optimization are adopted which can avoid unsolvability or converging difficulty. The results demonstrate the applicability and effectiveness of the proposed design methodology. 2012 IEEE.
	An improved hyperspectral classification algorithm based on back-propagation neural networks (EI CONFERENCE) 会议论文 OAI收割 2012 2nd International Conference on Remote Sensing, Environment and Transportation Engineering, RSETE 2012, June 1, 2012 - June 3, 2012, Nanjing, China 作者: Yu P.; Yu P. 收藏 \| 浏览/下载：30/0 \| 提交时间：2013/03/25 In this paper a new method is proposed to improve the classification performance of hyperspectral images by combining the principal component analysis (PCA) genetic algorithm (GA) and artificial neural networks (ANNs). First some characteristics of the hyperspectral remotely sensed data such as high correlation high redundancy etc. are investigated. Based on the above analysis we propose to use the principal component analysis to capture the main information existing in the hyperspectral images and reduce its dimensionality consequently. Next we use neural networks to classify the reduced hyperspectral data. Since the back-propagation neural network we used is easy to suffer from the local minimum problem we adopt a genetic algorithm to optimize the BP network's weights and the threshold. Experimental results show that the classification accuracy is improved and the time of calculation is reduced as well. 2012 IEEE.