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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 neuralnetworks (RBF) is applied into this area.

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

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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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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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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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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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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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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 applicationdecoupled dimension reduction method of high-dimensional system and reveal the calculation mechanism of the neuralthat a simple linear mapping relationship exists between network structure and network behavior in the neuralnew interpretation mechanism provides not only the potential mathematical calculation principle of neuralor animal activities, which can further expand and enrich the interpretable mechanism of artificial neural

Keywords： neural networks     interpretability     dynamical behavior     network decouple    

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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 the

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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Predicting the yield of pomegranate oil from supercritical extraction using artificial neural networks

J. Sargolzaei, A. Hedayati Moghaddam

Frontiers of Chemical Science and Engineering 2013, Volume 7, Issue 3,   Pages 357-365 doi: 10.1007/s11705-013-1336-3

Abstract: Several simulation systems including a back-propagation neural network (BPNN), a radial basis functionneural network (RBFNN) and an adaptive-network-based fuzzy inference system (ANFIS) were tested andThe performance of these networks was evaluated using the coefficient of determination ( ) and the mean

Keywords： oil recovery     artificial intelligence     extraction     neural networks     supercritical extraction    

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The use of Artificial Neural Networks to estimate seismic damage and derive vulnerability functions for

Tiago Miguel FERREIRA, João ESTÊVÃO, Rui MAIO, Romeu VICENTE

Frontiers of Structural and Civil Engineering 2020, Volume 14, Issue 3,   Pages 609-622 doi: 10.1007/s11709-020-0623-6

Abstract: grades obtained resorting to a classic damage formulation and an innovative approach based on Artificial NeuralNetworks (ANNs).

Keywords： Artificial Neural Networks     seismic vulnerability     masonry buildings     damage estimation     vulnerability curves    

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Predicting the strength properties of slurry infiltrated fibrous concrete using artificial neural network

T. Chandra Sekhara REDDY

Frontiers of Structural and Civil Engineering 2018, Volume 12, Issue 4,   Pages 490-503 doi: 10.1007/s11709-017-0445-3

Abstract: This paper is aimed at adapting Artificial Neural Networks (ANN) to predict the strength properties of

Keywords： artificial neural networks     root mean square error     SIFCON     silica fume     metakaolin     steel fiber    

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Day-ahead electricity price forecasting using back propagation neural networks and weighted least square

S. Surender REDDY,Chan-Mook JUNG,Ko Jun SEOG

Frontiers in Energy 2016, Volume 10, Issue 1,   Pages 105-113 doi: 10.1007/s11708-016-0393-y

Abstract: This paper proposes the day-ahead electricity price forecasting using the artificial neural networks

Keywords： day-ahead electricity markets     price forecasting     load forecasting     artificial neural networks     load serving    

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Unconfined compressive strength prediction of soils stabilized using artificial neural networks and support

Alireza TABARSA, Nima LATIFI, Abdolreza OSOULI, Younes BAGHERI

Frontiers of Structural and Civil Engineering 2021, Volume 15, Issue 2,   Pages 520-536 doi: 10.1007/s11709-021-0689-9

Abstract: Two artificial-intelligence-based models including artificial neural networks and support vector machinessupport vector machines in predicting the strength of the investigated soils compared with artificial neuralnetworks.

Keywords： unconfined compressive strength     artificial neural network     support vector machine     predictive models     regression    

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Title Author Date Type Operation

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

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

Wu Aiguo,Hao Runsheng

Journal Article

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

A. CHITRA,S. HIMAVATHI

Journal Article

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

Chang CHEN,Danmei XIE,Yangheng XIONG,Hengliang ZHANG

Journal Article

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

Qin Wei,Qin Shuyu

Journal Article

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

Cai Anhui,Liu Yonggang,Sun Guoxiong

Journal Article

Novel interpretable mechanism of neural networks based on network decoupling method

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

Predicting the yield of pomegranate oil from supercritical extraction using artificial neural networks

J. Sargolzaei, A. Hedayati Moghaddam

Journal Article

The use of Artificial Neural Networks to estimate seismic damage and derive vulnerability functions for

Tiago Miguel FERREIRA, João ESTÊVÃO, Rui MAIO, Romeu VICENTE

Journal Article

Predicting the strength properties of slurry infiltrated fibrous concrete using artificial neural network

T. Chandra Sekhara REDDY

Journal Article

Day-ahead electricity price forecasting using back propagation neural networks and weighted least square

S. Surender REDDY,Chan-Mook JUNG,Ko Jun SEOG

Journal Article

Unconfined compressive strength prediction of soils stabilized using artificial neural networks and support

Alireza TABARSA, Nima LATIFI, Abdolreza OSOULI, Younes BAGHERI

Journal Article