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Optimization of turbine cold-end system based on BP neural network and genetic algorithm

Chang CHEN,Danmei XIE,Yangheng XIONG,Hengliang ZHANG

Frontiers in Energy 2014, Volume 8, Issue 4,   Pages 459-463 doi: 10.1007/s11708-014-0335-5

Abstract: ultra-supercritical (USC) unit, the turbine cold-end system, was performed utilizing the back propagation (BP) neural network method with genetic algorithm (GA) optimization analysis.

Keywords： optimization     turbine     cold-end system     BP neural network     genetic algorithm    

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Research on the Forecast of the BP Neural Network Based on the Orthogonal Test

Cai Anhui,Liu Yonggang,Sun Guoxiong

Strategic Study of CAE 2003, Volume 5, Issue 7,   Pages 67-71

Abstract:

The strategy for forecasting the BP neural network was researched on the basis of the training-studyingwhose factors were the same as that of the self-contained orthogonal sample could be forecast in the BPneural network and its precision was considerable high.

Keywords： BP neural network     orthogonal test     strategy     design-test approach     sample collection    

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Study of Forecast of Building Cost Based on Neural Network

Nie Guihua,Liu Pingfeng,He Liu

Strategic Study of CAE 2005, Volume 7, Issue 10,   Pages 56-59

Abstract: To solve this problem, this paper adopts the model of the back-propagation neural network, takes thefeatures of construction as input variables, trains the network using actual data as samples and optimizesthe network structure by contribution analysis.

Keywords： BP neural network     building budget     forecast    

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Research on Nonlinear Combination Forecasting Approach Based on BP-AGA

Wang Shuo,Zhang Youfu,Jin Juliang

Strategic Study of CAE 2005, Volume 7, Issue 4,   Pages 83-87

Abstract:

A nonlinear combination forecasting model was established by using neural network and acceleratingAGA was used to optimize the network parameters as BP approach was slow with training network.Optimization results of AGA were taken as original values of BP approach, the network was trained withBP approach.Network convergence rate was increased with running BP approach and AGA alternately.

Keywords： neural network     accelerating genetic algorithm     nonlinear combination forecasting     forecasting precision    

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An Improving Method of BP Neural Network and Its Application

Li Honggang,Lü Hui,Li Gang

Strategic Study of CAE 2005, Volume 7, Issue 5,   Pages 63-65

Abstract:

Seeing on that in BPNN the small learning gene will make the long training time, but the large learning gene will make the BPNN surging, this paper brings forward a way to modify the learning gene, that is, adding a proportion gene before the learning gene, The proportion gene will change when the weight of the BPNN needs to be modified. This can shorten the training time and make convergence better as well. The simulating results show that the new algorithm is much better than the old one during BPNN scouting the missile command.

Keywords： BPNN     improved algorithm     simulation    

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The Safe and Quick Long-Distance Transmission MethodBased on BP Neural Net for Engineering Graphics Data

Qin Wei,Qin Shuyu

Strategic Study of CAE 2007, Volume 9, Issue 1,   Pages 49-52

Abstract: is built and data code compression and data encryption are put in practice at the same time by using BPalgorithm of artificial neural network.Examples show that this method can be used in actual engineering

Keywords： neural net     BP algorithm     correlation     encrypt     speed transmission     graphics data    

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Study on the Purification of Wastewater in the Constructed Wetland Based on GA－BP Network

Huang Juan,Wang Shihe,Luo Weiguo,Qian Weiyi ,Yan Lu

Strategic Study of CAE 2007, Volume 9, Issue 2,   Pages 79-83

Abstract: Based on plenty of reliable experimental data, genetic neural network was first tentatively utilizedOptimized GA-BP network was established to simulate orthogonal test of wetland system.

Keywords： constructed wetlands     wastewater purification     GA-BP network     orthogonal test    

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Intelligent Forecasting Mode and Approach of Mid and Long Term Intelligent Hydrological Forecasting

Chen Shouyu,Guo Yu,Wang Dagang

Strategic Study of CAE 2006, Volume 8, Issue 7,   Pages 30-35

Abstract:

Intelligent calculating tools such as fuzzy optimization approaches, BP neural network and geneticthese approaches, and then, in this paper, the author organically synthesizes fuzzy optimal selection, BPneural network and genetic algorithm and establishes intelligent forecasting mode and method.the correlation of forecasting factors and forecasting objective, then takes the matrix as input of BPneural network to train link-weights, and finally, uses gained link-weight values to verify forecasting

Keywords： fuzzy optimal selection     BP neural network     genetic algorithm     intelligent forecasting mode     mid and long    

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An Improved BP Algorithm Applying to Inverse Kinematics Problems of Robot Manipulator

Wu Aiguo,Hao Runsheng

Strategic Study of CAE 2005, Volume 7, Issue 7,   Pages 34-38

Abstract: paper, an algorithm in which active function is improved is proposed through analyzing the conventional BPThe multilayer forward neural networks are used to establish the inverse kinematics models for robotmanipulator by this improved BP algorithm.and improves the inverse kinematics solutions for robot manipulator as compared to the conventional BP

Keywords： neural networks     BP algorithm     active function     robot manipulator     inverse kinematics    

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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: The neural networks can be used to construct fully decoupled approaches in nonlinear multiscale methodsThis article intends to model the multiscale constitution using feedforward neural network (FNN) andrecurrent neural network (RNN), and appropriate set of loading paths are selected to effectively predict

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

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A pre-compensation method of the systematic contouring error for repetitive command paths

D. L. ZHANG,Y. H. CHEN,Y. P. CHEN

Frontiers of Mechanical Engineering 2015, Volume 10, Issue 4,   Pages 367-372 doi: 10.1007/s11465-015-0367-5

Abstract: the pre-compensation value with better accuracy, this paper proposes the use of a back propagation neuralnetwork to extract the function of systematic contouring errors.

Keywords： contouring error     pre-compensation     motion control system     back propagation (BP) neural network    

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A modified neural learning algorithm for online rotor resistance estimation in vector controlled induction

A. CHITRA,S. HIMAVATHI

Frontiers in Energy 2015, Volume 9, Issue 1,   Pages 22-30 doi: 10.1007/s11708-014-0339-1

Abstract: In this paper, a novel modified neural algorithm has been identified for the online estimation of rotorNeural based estimators are now receiving active consideration as they have a number of advantages overThe training algorithm of the neural network determines its learning speed, stability, weight convergenceIn this paper, the neural estimator has been studied with conventional and proposed learning algorithms

Keywords： neural networks     back propagation (BP)     rotor resistance estimators     vector control     induction motor    

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Hydrogeological Parameter Identification Based on the Radial Basis Function Neural Networks

Zhang Junyan,Wei Lianwei,Han Weixiu,Shao Jingli,Cui Yali,Zhang Jianli

Strategic Study of CAE 2004, Volume 6, Issue 8,   Pages 74-78

Abstract: With the limit of identifying the parameter by traditional methods, the radial basis function neural

Keywords： groundwater     hydrogeological parameter     radial basis function (RBF) neural networks     BP neural networks    

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A new automatic convolutional neural network based on deep reinforcement learning for fault diagnosis

Frontiers of Mechanical Engineering 2022, Volume 17, Issue 2, doi: 10.1007/s11465-022-0673-7

Abstract: Convolutional neural network (CNN) has achieved remarkable applications in fault diagnosis.

Keywords： deep reinforcement learning     hyper parameter optimization     convolutional neural network     fault diagnosis    

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Novel interpretable mechanism of neural networks based on network decoupling method

Frontiers of Engineering Management 2021, Volume 8, Issue 4,   Pages 572-581 doi: 10.1007/s42524-021-0169-x

Abstract: The lack of interpretability of the neural network algorithm has become the bottleneck of its wide applicationnetwork.Result shows that a simple linear mapping relationship exists between network structure and network behaviorin the neural network with high-dimensional and nonlinear characteristics.which can further expand and enrich the interpretable mechanism of artificial neural network in the future

Keywords： neural networks     interpretability     dynamical behavior     network decouple    

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Title Author Date Type Operation

Optimization of turbine cold-end system based on BP neural network and genetic algorithm

Chang CHEN,Danmei XIE,Yangheng XIONG,Hengliang ZHANG

Journal Article

Research on the Forecast of the BP Neural Network Based on the Orthogonal Test

Cai Anhui,Liu Yonggang,Sun Guoxiong

Journal Article

Study of Forecast of Building Cost Based on Neural Network

Nie Guihua,Liu Pingfeng,He Liu

Journal Article

Research on Nonlinear Combination Forecasting Approach Based on BP-AGA

Wang Shuo,Zhang Youfu,Jin Juliang

Journal Article

An Improving Method of BP Neural Network and Its Application

Li Honggang,Lü Hui,Li Gang

Journal Article

The Safe and Quick Long-Distance Transmission MethodBased on BP Neural Net for Engineering Graphics Data

Qin Wei,Qin Shuyu

Journal Article

Study on the Purification of Wastewater in the Constructed Wetland Based on GA－BP Network

Huang Juan,Wang Shihe,Luo Weiguo,Qian Weiyi ,Yan Lu

Journal Article

Intelligent Forecasting Mode and Approach of Mid and Long Term Intelligent Hydrological Forecasting

Chen Shouyu,Guo Yu,Wang Dagang

Journal Article

An Improved BP Algorithm Applying to Inverse Kinematics Problems of Robot Manipulator

Wu Aiguo,Hao Runsheng

Journal Article

Multiscale computation on feedforward neural network and recurrent neural network

Bin LI, Xiaoying ZHUANG

Journal Article

A pre-compensation method of the systematic contouring error for repetitive command paths

D. L. ZHANG,Y. H. CHEN,Y. P. CHEN

Journal Article

A modified neural learning algorithm for online rotor resistance estimation in vector controlled induction

A. CHITRA,S. HIMAVATHI

Journal Article

Hydrogeological Parameter Identification Based on the Radial Basis Function Neural Networks

Zhang Junyan,Wei Lianwei,Han Weixiu,Shao Jingli,Cui Yali,Zhang Jianli

Journal Article

A new automatic convolutional neural network based on deep reinforcement learning for fault diagnosis

Journal Article

Novel interpretable mechanism of neural networks based on network decoupling method

Journal Article