中国科学院机构知识库网格系统: 检索

Data Generation Feedback Relearning Control for Unmodeled Nonlinear Systems 期刊论文 OAI收割
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE, 2023, 页码: 12
作者: Zhang, Yong

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收藏 | 浏览/下载：21/0 | 提交时间：2023/11/16

Data models Real-time systems Heuristic algorithms Mathematical models Adaptation models Approximation algorithms Cost function Data generation model feedback relearning control delayed neural network reinforcement learning unmodeled nonlinear system

Deep Nitsche Method: Deep Ritz Method with Essential Boundary Conditions 期刊论文 OAI收割
COMMUNICATIONS IN COMPUTATIONAL PHYSICS, 2021, 卷号: 29, 期号: 5, 页码: 1365-1384
作者: Liao, Yulei; Ming, Pingbing

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收藏 | 浏览/下载：22/0 | 提交时间：2021/06/01

Deep Nitsche Method Deep Ritz Method neural network approximation mixed boundary conditions curse of dimensionality

Adaptive Neural Control of a Class of Stochastic Nonlinear Uncertain Systems With Guaranteed Transient Performance 期刊论文 OAI收割
IEEE TRANSACTIONS ON CYBERNETICS, 2020, 卷号: 50, 期号: 7, 页码: 2971-2981
作者: Wang, Jianhui; Liu, Zhi; Zhang, Yun; Chen, C. L. Philip; Lai, Guanyu

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收藏 | 浏览/下载：22/0 | 提交时间：2020/08/03

Neural networks Adaptive systems Nonlinear systems Backstepping Transient analysis Approximation error Time-varying systems Adaptive control neutral network nonlinear control stochastic disturbance transient performance

Better Approximations of High Dimensional Smooth Functions by Deep Neural Networks with Rectified Power Units 期刊论文 OAI收割
COMMUNICATIONS IN COMPUTATIONAL PHYSICS, 2020, 卷号: 27, 期号: 2, 页码: 379-411
作者: Li, Bo; Tang, Shanshan; Yu, Haijun

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收藏 | 浏览/下载：9/0 | 提交时间：2020/05/24

Deep neural network high dimensional approximation sparse grids rectified linear unit rectified power unit rectified quadratic unit

Adaptive dynamic programming for robust neural control of unknown continuous-time non-linear systems 期刊论文 OAI收割
IET CONTROL THEORY AND APPLICATIONS, 2017, 卷号: 11, 期号: 14, 页码: 2307-2316
作者: Yang, Xiong; He, Haibo; Liu, Derong; Zhu, Yuanheng

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收藏 | 浏览/下载：36/0 | 提交时间：2017/09/13

Dynamic Programming Robust Control Neurocontrollers Continuous Time Systems Control System Synthesis Nonlinear Control Systems Optimal Control Function Approximation Monte Carlo Methods Closed Loop Systems Asymptotic Stability Adaptive Dynamic Programming Robust Neural Control Design Unknown Continuous-time Nonlinear Systems Ct Nonlinear Systems Adp-based Robust Neural Control Scheme Robust Nonlinear Control Problem Nonlinear Optimal Control Problem Nominal System Adp Algorithm Actor-critic Dual Networks Control Policy Approximation Value Function Approximation Actor Neural Network Weights Critic Nn Weights Monte Carlo Integration Method Closed-loop System Asymptotically Stability

Echo state network-based Q-learning method for optimal battery control of offices combined with renewable energy 期刊论文 OAI收割
IET CONTROL THEORY AND APPLICATIONS, 2017, 卷号: 11, 期号: 7, 页码: 915-922
作者: Shi, Guang; Liu, Derong; Wei, Qinglai

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收藏 | 浏览/下载：22/0 | 提交时间：2017/02/23

Recurrent Neural Nets Neurocontrollers Learning (Artificial Intelligence) Office Environment Optimal Control Solar Power Energy Consumption Time Series Secondary Cells Energy Management Systems Function Approximation Echo State Network-based Q-learning Method Optimal Battery Control Renewable Energy Optimal Energy Management Solar Energy Energy Consumption Energy Demand Time Series Real-time Electricity Rate Periodic Functions Q-function Optimal Charging Strategy Optimal Discharging Strategy Optimal Idle Strategy Numerical Analysis

Neural-network-based online optimal control for uncertain non-linear continuous-time systems with control constraints 期刊论文 OAI收割
IET CONTROL THEORY AND APPLICATIONS, 2013, 卷号: 7, 期号: 17, 页码: 2037-2047
作者: Yang, Xiong; Liu, Derong; Huang, Yuzhu

收藏 | 浏览/下载：32/0 | 提交时间：2015/08/12

adaptive control approximation theory closed loop systems continuous time systems Lyapunov methods neurocontrollers nonlinear control systems optimal control robust control uncertain systems neural network-based online adaptive optimal control uncertain nonlinear continuous-time systems control constraints infinite-horizon optimal control problem control policy saturation constraints identifier-critic architecture Hamilton-Jacobi-Bellman equation approximation uncertain system dynamics critic NN action-critic dual networks reinforcement learning identifier NN policy iteration LyapunovaEuros direct method closed loop system stability

Study of experimental design and Response Surface method for surrogate model of computational simulation (EI CONFERENCE) 会议论文 OAI收割
2nd Annual Conference on Electrical and Control Engineering, ICECE 2011, September 16, 2011 - September 18, 2011, Yichang, China
Xi R.; Jia H.; Xiao Q.

收藏 | 浏览/下载：21/0 | 提交时间：2013/03/25

While the high-precision simulation is widely used in science and technology Design of Experiment (DOE) based on Response Surface (RS) method can be employed in surrogate model to reduce the cost and error. In order to illustrate the relationship between parameters and response features several DOE methods and Response Surface (RS) method are studied. The author used polynomial regression and RBF neural network based on orthogonal array to build a rocket aerodynamic discipline surrogate model respectively which proved their feasibility. From the results of the test case conclusion is drawn that characteristic as well as acclimatization of DOE methods and different approximation should be considered for different issues so the factors of cost and accuracy could reach a balance synthetically. 2011 IEEE.

Research on the identification for a nonlinear system (EI CONFERENCE) 会议论文 OAI收割
International Conference on Optical, Electronic Materials and Applications 2011, OEMA 2011, March 4, 2011 - March 6, 2011, Chongqing, China
作者: Liu J.; Jia P.; Liu J.; Liu J.

收藏 | 浏览/下载：32/0 | 提交时间：2013/03/25

The characteristic of the drift error of inertial platform is a high-order nonlinear dynamic system using the neural networks' abilities of universal approximation of differentiable trajectory and capturing system dynamic information this paper presents the drift error identifying project of inertial platform based on Elman networks structure. First the drift error model of inertial platform is established after selecting the input and output for network momentum and alterable speed algorithm is used to speed up the network convergence. On the basis of the algorithm the extended nonlinear node function in the hidden network does not only improve the learning speed of network but also satisfies the need of accuracy on system identification. Through the drift error data measured on inertial platform the training result shows that the scheme achieves satisfied identification results. (2011) Trans Tech Publications.

Correcting the systematic error of the density functional theory calculation: the alternate combination approach of genetic algorithm and neural network 期刊论文 OAI收割
chinese physics b, CHINESE PHYSICS B, 2010, 2010, 卷号: 19, 19, 期号: 7, 页码: art. no. 076401, Art. No. 076401
作者: Wang TT (Wang Ting-Ting); Li WL (Li Wen-Long); Chen ZH (Chen Zhang-Hui); Miao L (Miao Ling); Chen, ZH, Chinese Acad Sci, Inst Semicond, State Key Lab Superlattices & Microstruct, Beijing 100083, Peoples R China. 电子邮箱地址: zhanghuichen88@gmail.com

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收藏 | 浏览/下载：102/1 | 提交时间：2010/08/17

density functional theory Density Functional Theory Neural Network Genetic Algorithm Alternate Combination Linear-regression Correction Training Set Electron-gas Prediction Approximation Descriptors Accurate Energy Heat neural network genetic algorithm alternate combination LINEAR-REGRESSION CORRECTION TRAINING SET ELECTRON-GAS PREDICTION APPROXIMATION DESCRIPTORS ACCURATE ENERGY HEAT