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Deep feedforward neural network 1

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Multiscale computation on feedforward neural network and recurrent neural network

Bin LI, Xiaoying ZHUANG

Frontiers of Structural and Civil Engineering 2020, Volume 14, Issue 6,   Pages 1285-1298 doi: 10.1007/s11709-020-0691-7

Abstract: This article intends to model the multiscale constitution using feedforward neural network (FNN) and

Keywords： multiscale method     constitutive model     feedforward neural network     recurrent neural network    

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Feedforward control for nitrogen removal in a pilot-scale anaerobic-anoxic-oxic plant for municipal wastewater

Tonggang SHEN, Hanchang SHI, Huiming SHI, He JING, Huilei XIONG

Frontiers of Environmental Science & Engineering 2011, Volume 5, Issue 1,   Pages 130-139 doi: 10.1007/s11783-010-0266-2

Abstract: To improve the efficiency of nitrogen removal with lower energy consumption, the study of feedforwardSimple feedforward control strategies for aeration and internal recycle were subsequently proposed and

Keywords： anaerobic-anoxic-oxic (AAO)     feedforward control     municipal wastewater     nitrogen removal    

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Constant temperature control of tundish induction heating power supply for metallurgical manufacturing

Yufei YUE, Qianming XU, Peng GUO, An LUO

Frontiers in Energy 2019, Volume 13, Issue 1,   Pages 16-26 doi: 10.1007/s11708-018-0572-0

Abstract: Besides, an input harmonic current blocking strategy and a load voltage feedforward control are also

Keywords： heating power supply (TIHPS)     constant temperature control     input harmonic current blocking     load voltage feedforward    

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High-accuracy target tracking for multistatic passive radar based on a deep feedforward neural network Research Article

Baoxiong XU, Jianxin YI, Feng CHENG, Ziping GONG, Xianrong WAN,jessiexu@whu.edu.cn,jxyi@whu.edu.cn,cwing@whu.edu.cn

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 8,   Pages 1214-1230 doi: 10.1631/FITEE.2200260

Abstract: In radar systems, errors are mainly from motion models and nonlinear measurements. When we evaluate a tracking algorithm, its is the main criterion. To improve the , in this paper we formulate the tracking problem into a regression model from measurements to target states. A tracking algorithm based on a modified (MDFNN) is then proposed. In MDFNN, a is introduced to describe the temporal sequence relationship of the input measurement sequence, and the optimal measurement sequence size is analyzed. Simulations and field experimental data of the show that the accuracy of the proposed algorithm is better than those of extended Kalman filter (EKF), unscented Kalman filter (UKF), and recurrent neural network (RNN) based tracking methods under the considered scenarios.

Keywords： Deep feedforward neural network     Filter layer     Passive radar     Target tracking     Tracking accuracy    

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Development of a Novel Feedforward Neural Network Model Based on Controllable Parameters for Predicting Article

Zihao Zhao, Zihao Wang, Jialuo Yuan, Jun Ma, Zheling He, Yilan Xu, Xiaojia Shen, Liang Zhu

Engineering 2021, Volume 7, Issue 2,   Pages 195-202 doi: 10.1016/j.eng.2020.07.027

Abstract: improve the model controllability, and ② the model prediction accuracy is improved on the basis of a feedforward

Keywords： Feedforward neural network (FFNN)     Algorithms     Controllable operation parameters     Sequencing batch reactor    

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Research on the control systems of a precision linear motor drive device

YAN Han, WANG Xian-kui, DUAN Guang-hong

Frontiers of Mechanical Engineering 2006, Volume 1, Issue 3,   Pages 260-264 doi: 10.1007/s11465-006-0036-9

Abstract: of the system, its third-order model is established, and disturbance-observer based input revising feedforwardThis includes feedback control algorithm and feedforward control algorithm.controller improves system tracking performance and suppresses load and mechanical disturbance while the feedforward

Keywords： feedforward     tracking     ±5     anti-disturbance performance     algorithm    

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Fault-Tolerant Control of a CPG-Governed Robotic Fish Article

Yueqi Yang, Jian Wang, Zhengxing Wu, Junzhi Yu

Engineering 2018, Volume 4, Issue 6,   Pages 861-868 doi: 10.1016/j.eng.2018.09.011

Abstract: The created control system is composed of two main components: a feedback controller and a feedforwardfeedback controller is designed to make the robotic fish robust to external disturbances, while the feedforward

Keywords： Fault-tolerant control     Robotic fish     Motion control     Feedback controller     Feedforward compensator    

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Engram-Driven Videography Article

Lu Fang, Mengqi Ji, Xiaoyun Yuan, Jing He, Jianing Zhang, Yinheng Zhu, Tian Zheng, Leyao Liu, Bin Wang, Qionghai Dai

Engineering 2023, Volume 25, Issue 6,   Pages 101-109 doi: 10.1016/j.eng.2021.12.012

Abstract: Moreover, they strictly follow the feedforward pathway: that is, their pixel-level sensing is independentDifferently, a human visual system owns superiority with both feedforward and feedback pathways: Thefeedforward pathway extracts object representation (referred to as memory engram) from visual inputs,

Keywords： Instance-level videography     Memory engram     Large-scale dynamic scene     Feedforward and feedback     Entropy    

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Title Author Date Type Operation

Multiscale computation on feedforward neural network and recurrent neural network

Bin LI, Xiaoying ZHUANG

Journal Article

Feedforward control for nitrogen removal in a pilot-scale anaerobic-anoxic-oxic plant for municipal wastewater

Tonggang SHEN, Hanchang SHI, Huiming SHI, He JING, Huilei XIONG

Journal Article

Constant temperature control of tundish induction heating power supply for metallurgical manufacturing

Yufei YUE, Qianming XU, Peng GUO, An LUO

Journal Article

High-accuracy target tracking for multistatic passive radar based on a deep feedforward neural network

Baoxiong XU, Jianxin YI, Feng CHENG, Ziping GONG, Xianrong WAN,jessiexu@whu.edu.cn,jxyi@whu.edu.cn,cwing@whu.edu.cn

Journal Article

Development of a Novel Feedforward Neural Network Model Based on Controllable Parameters for Predicting

Zihao Zhao, Zihao Wang, Jialuo Yuan, Jun Ma, Zheling He, Yilan Xu, Xiaojia Shen, Liang Zhu

Journal Article

Research on the control systems of a precision linear motor drive device

YAN Han, WANG Xian-kui, DUAN Guang-hong

Journal Article

Fault-Tolerant Control of a CPG-Governed Robotic Fish

Yueqi Yang, Jian Wang, Zhengxing Wu, Junzhi Yu

Journal Article

Engram-Driven Videography

Lu Fang, Mengqi Ji, Xiaoyun Yuan, Jing He, Jianing Zhang, Yinheng Zhu, Tian Zheng, Leyao Liu, Bin Wang, Qionghai Dai

Journal Article