中国科学院机构知识库网格系统: 检索

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	Intelligent fault diagnosis of train bearing based on ISTOA-VMD and SE-WDCNN 期刊论文 OAI收割 JOURNAL OF VIBRATION AND CONTROL, 2023, 页码: 12 作者: He, Deqiang; Zou, Xueyan; Jin, Zhenzhen; Yan, Jingren; Ren, Chonghui \| 收藏 \| 浏览/下载：19/0 \| 提交时间：2023/12/07 intelligent fault diagnosis train bearing improved sooty tern optimization algorithm variational mode decomposition deep convolutional neural network
	A Dragonfly Optimization Algorithm for Extracting Maximum Power of Grid-Interfaced PV Systems 期刊论文 OAI收割 SUSTAINABILITY, 2021, 卷号: 13, 期号: 19, 页码: 27 作者: Lodhi, Ehtisham; Wang, Fei-Yue; Xiong, Gang; Mallah, Ghulam Ali; Javed, Muhammad Yaqoob \| 收藏 \| 浏览/下载：29/0 \| 提交时间：2022/01/27 photovoltaic (PV) partial shading maximum power point tracking (MPPT) dragonfly optimization algorithm (DOA) adaptive cuckoo search optimization (ACSO) fruit fly optimization algorithm combined with general regression neural network (FFO-GRNN) improved particle swarm optimization (IPSO) voltage source inverter (VSI) total harmonic distortion (THD)
	Improved Neural Network 3D Space Obstacle Avoidance Algorithm for Mobile Robot 会议论文 OAI收割 Shenyang, China, August 8-11, 2019 作者: Tong YC(佟玉闯); Liu JG(刘金国); Liu YW(刘玉旺) \| 收藏 \| 浏览/下载：30/0 \| 提交时间：2019/09/05 Global path planning Obstacle avoidance algorithm Improved neural network algorithm Adaptive variable stepsize Simulated annealing
	Numerical Prediction of Self-propulsion Point of AUV with a Discretized Propeller and MFR Method 会议论文 OAI收割 Shenyang, China, August 8-11, 2019 作者: Wu LH(吴利红); Feng XS(封锡盛); Sun, Xiannian; Zhou, Tongming \| 收藏 \| 浏览/下载：14/0 \| 提交时间：2019/09/05 Global path planning Obstacle avoidance algorithm Improved neural network algorithm Adaptive variable stepsize Simulated annealing
	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：31/0 \| 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.
	Double inverted pendulum control based on three-loop PID and improved BP neural network (EI CONFERENCE) 会议论文 OAI收割 2011 2nd International Conference on Digital Manufacturing and Automation, ICDMA 2011, August 5, 2011 - August 7, 2011, Zhangjiajie, Hunan, China 作者: Fan Y. 收藏 \| 浏览/下载：35/0 \| 提交时间：2013/03/25 To deal with the defects of BP neural networks used in balance control of inverted pendulum such as longer train time and converging in partial minimum this article reaLizes the control of double inverted pendulum with improved BP algorithm of artificial neural networks(ANN) builds up a training model of test simulation and the BP network is 6-10-1 structure. Tansig function is used in hidden layer and PureLin function is used in output layer LM is used in training algorithm. The training data is acquried by three-loop PID algorithm. The model is learned and trained with Matlab calculating software and the simuLink simulation experiment results prove that improved BP algorithm for inverted pendulum control has higher precision better astringency and lower calculation. This algorithm has wide appLication on nonLinear control and robust control field in particular. 2011 IEEE.
	The research of nonlinear control based on fuzzy neural network (EI CONFERENCE) 会议论文 OAI收割 International Conference on Electrical and Control Engineering, ICECE 2010, June 26, 2010 - June 28, 2010, Wuhan, China Fan Y.-Y.; Sang Y.-J. 收藏 \| 浏览/下载：24/0 \| 提交时间：2013/03/25 This paper discussed and researched the structure and algorithm of fuzzy neural network controller based on the character of fuzzy logic and neural network theory. For the nonlinear system characteristics of uncertainty high order and hysteresis this paper used the fuzzy neural network technology to control nonlinear system and improved the control quality obviously. Take the single inverted pendulum for example the paper constructed the nonlinear mathematicmodel realized the control with the method of the adaptive fuzzy neural network and compared with control method of liner quadratic regulator the simulation results indicate that the method of adaptive fuzzy neural network can realize the stabilization of control better without the linear model of system and has a higher robustness. 2010 IEEE.