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Rapid Prediction of Effect of Localized Spallation of Thermal Barrier Coatings on Blade Cooling Efficiency Based on an MLP Neural Network 认领 引用
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作者 ZHANG Yeling WANG Feilong +2 位作者 WANG Yuqun WANG Yubin MAO Junkui 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI CSCD 2025年第6期813-829,共17页
The study of the spallation of thermal barrier coatings on turbine blades and its influence is of great significance for gas turbine safety operation.However,numerical simulation related to thermal barrier coatings is... The study of the spallation of thermal barrier coatings on turbine blades and its influence is of great significance for gas turbine safety operation.However,numerical simulation related to thermal barrier coatings is difficult and time-costly,which makes it hard to meet engineering demands.Therefore,this work establishes a rapid prediction model for the surface temperature and cooling efficiency of turbine blades with localized spallation of thermal barrier coatings based on a thin-wall thermal resistance model.Firstly,the influence of localized spallation of thermal barrier coatings on the cooling efficiency of typical turbine blades is numerically investigated.Then,based on the simulation data set and multi-layer perception(MLP)neural network,an intelligent prediction model for the temperature and cooling efficiency distribution of localized spallation of coatings is constructed,which can rapidly predict the surface temperature and cooling efficiency of the blade under the situation of spallation of coating at any position on the blade surface.The results show that,under a certain spallation area,the shape of localized coating spallation has little influence on the cooling efficiency,while the increase of spallation thickness will cause a linear increase in the average temperature of the blade surface.The prediction error of the proposed rapid prediction model for the average surface temperature and cooling efficiency of blades is within 2%,and the prediction error of the temperature and cooling efficiency at the spallation position is within 6%for 80%of the samples,with an overall average error within 10%.It is concluded from the rapid prediction model that when the depth of coating spallation increases,the closer the spallation position is to the leading edge of the blade,the greater the difference in cooling efficiency is,and the degree of influence of coating spallation on the cooling efficiency also increases. 展开更多
关键词 thermal barrier coating(TBC) cooling performance rapid prediction multi-layer perception(MLP)neural network
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Accurate Classification of EEG Signals Using Neural Networks Trained by Hybrid Populationphysic-based Algorithm 认领 引用 被引量：7
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作者 Sajjad Afrakhteh Mohammad-Reza Mosavi +1 位作者 Mohammad Khishe Ahmad Ayatollahi 《International Journal of Automation and computing》 CSCD 2020年第1期108-122,共15页
A brain-computer interface(BCI)system is one of the most effective ways that translates brain signals into output commands.Different imagery activities can be classified based on the changes inμandβrhythms and their... A brain-computer interface(BCI)system is one of the most effective ways that translates brain signals into output commands.Different imagery activities can be classified based on the changes inμandβrhythms and their spatial distributions.Multi-layer perceptron neural networks(MLP-NNs)are commonly used for classification.Training such MLP-NNs has great importance in a way that has attracted many researchers to this field recently.Conventional methods for training NNs,such as gradient descent and recursive methods,have some disadvantages including low accuracy,slow convergence speed and trapping in local minimums.In this paper,in order to overcome these issues,the MLP-NN trained by a hybrid population-physics-based algorithm,the combination of particle swarm optimization and gravitational search algorithm(PSOGSA),is proposed for our classification problem.To show the advantages of using PSOGSA that trains NNs,this algorithm is compared with other meta-heuristic algorithms such as particle swarm optimization(PSO),gravitational search algorithm(GSA)and new versions of PSO.The metrics that are discussed in this paper are the speed of convergence and classification accuracy metrics.The results show that the proposed algorithm in most subjects of encephalography(EEG)dataset has very better or acceptable performance compared to others. 展开更多
关键词 Brain-computer interface(BCI) classification electroencephalography(EEG) gravitational search algorithm(GSA) multi-layer perceptron neural network(MLP-NN) particle swarm optimization
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TMC-GCN: Encrypted Traffic Mapping Classification Method Based on Graph Convolutional Networks 认领 引用 被引量：1
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作者 Baoquan Liu Xi Chen +2 位作者 Qingjun Yuan Degang Li Chunxiang Gu 《Computers, Materials & Continua》 SCIE EI 2025年第2期3179-3201,共23页
With the emphasis on user privacy and communication security, encrypted traffic has increased dramatically, which brings great challenges to traffic classification. The classification method of encrypted traffic based... With the emphasis on user privacy and communication security, encrypted traffic has increased dramatically, which brings great challenges to traffic classification. The classification method of encrypted traffic based on GNN can deal with encrypted traffic well. However, existing GNN-based approaches ignore the relationship between client or server packets. In this paper, we design a network traffic topology based on GCN, called Flow Mapping Graph (FMG). FMG establishes sequential edges between vertexes by the arrival order of packets and establishes jump-order edges between vertexes by connecting packets in different bursts with the same direction. It not only reflects the time characteristics of the packet but also strengthens the relationship between the client or server packets. According to FMG, a Traffic Mapping Classification model (TMC-GCN) is designed, which can automatically capture and learn the characteristics and structure information of the top vertex in FMG. The TMC-GCN model is used to classify the encrypted traffic. The encryption stream classification problem is transformed into a graph classification problem, which can effectively deal with data from different data sources and application scenarios. By comparing the performance of TMC-GCN with other classical models in four public datasets, including CICIOT2023, ISCXVPN2016, CICAAGM2017, and GraphDapp, the effectiveness of the FMG algorithm is verified. The experimental results show that the accuracy rate of the TMC-GCN model is 96.13%, the recall rate is 95.04%, and the F1 rate is 94.54%. 展开更多
关键词 Encrypted traffic classification deep learning graph neural networks multi-layer perceptron graph convolutional networks
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基于交易信息的GNN-MLP企业服务推荐算法 认领 引用
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作者 贾昊男 张健 +1 位作者 陈进东 何琼 《计算机应用与软件》 北大核心 2026年第3期125-131,共7页
基于中小微企业服务交易信息,构建一种融合图神经网络和多层感知机的企业服务推荐算法,解决中小微企业服务需求不明确问题。通过图神经网络学习企业和服务显性交互信息下的隐性特征关系;基于多层感知机挖掘企业和服务之间的非线性特征关... 基于中小微企业服务交易信息,构建一种融合图神经网络和多层感知机的企业服务推荐算法,解决中小微企业服务需求不明确问题。通过图神经网络学习企业和服务显性交互信息下的隐性特征关系;基于多层感知机挖掘企业和服务之间的非线性特征关系;融合特征,实现企业-服务之间不同维度的特征融合,缓解单个模型进行特征挖掘时特征缺失问题。在公共数据集和真实企业服务交易数据集上的实验表明,该算法在HR和NDCG评价指标上相比基线算法有较大提升,证明了特征融合可以提高特征挖掘效果和为企业进行服务推荐的有效性。 展开更多
关键词 交易信息 企业服务 图神经网络 多层感知机 推荐算法
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Design an Artificial Neural Network by MLP Method;Analysis of the Relationship between Demographic Variables, Resilience, COVID-19 and Burnout 认领 引用
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作者 Chao-Hsi Huang Tsung-Shun Hsieh +2 位作者 Hsiao-Ting Chien Ehsan Eftekhari-Zadeh Saba Amiri 《International Journal of Mental Health Promotion》 2022年第6期825-841,共17页
In addition to the effect that the COVID-19 pandemic has had on the physical and mental health of individuals,it has also led to a change in the mental and emotional state of many employees.Especially among businesses... In addition to the effect that the COVID-19 pandemic has had on the physical and mental health of individuals,it has also led to a change in the mental and emotional state of many employees.Especially among businesses and private companies,which faced many restrictions due to the special conditions of the pandemic.Therefore,the present study aimed to design an artificial neural network with MLP technique to analyze the relationship between demographic variables,resilience,COVID-19 and burnout in start-ups in Iran.The research method was quantitative.Managers and employees of start-ups formed the statistical population of the study,based on the statistical sample size of the unlimited community,384 of them were tested.For data gathering,standard questionnaires include of MBI-GS and BRCS and researcher-made questionnaire of stress caused by COVID-19 were used.The validity of the questionnaires was confirmed by a panel of experts and their reliability was confirmed by Cronbach’s alpha coefficient.The number of neurons in the input layer was equal to 10,the number of neurons in the 1st hidden layer was equal to 7,the number of neurons in the output layer was equal to 1,and the number of epochs was equal to 500.70%of the data were used for training and 30%for testing.In the designed artificial neural network,all experiment data except one were correctly predicted and the obtained MAE error was less than 0.012%.Finally,he precision and correction of the presented model was confirmed by the obtained results. 展开更多
关键词 Burnout artificial neural network multi-layer perceptron COVID-19 resilience
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Classification and Identification of Nuclear, Biological or Chemical Agents Taken from Remote Sensing Image by Using Neural Network 认领 引用
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作者 Said El Yamani Samir Zeriouh Mustapha Boutahri Ahmed Roukhe 《Journal of Physical Science and Application》 2014年第3期177-182,共6页
In the context of new risks and threats associated to nuclear, biological and chemical （NBC） attacks, and given the shortcomings of certain analytical methods such as principal component analysis （PCA）, a neural n... In the context of new risks and threats associated to nuclear, biological and chemical （NBC） attacks, and given the shortcomings of certain analytical methods such as principal component analysis （PCA）, a neural network approach seems to be more accurate. PCA consists in projecting the spectrum of a gas collected from a remote sensing system in, firstly, a three-dimensional space, then in a two-dimensional one using a model of Multi-Layer Perceptron based neural network. It adopts during the learning process, the back propagation algorithm of the gradient, in which the mean square error output is continuously calculated and compared to the input until it reaches a minimal threshold value. This aims to correct the synaptic weights of the network. So, the Artificial Neural Network （ANN） tends to be more efficient in the classification process. This paper emphasizes the contribution of the ANN method in the spectral data processing, classification and identification and in addition, its fast convergence during the back propagation of the gradient. 展开更多
关键词 Artificial neural networks classification identification principal component analysis multi-layer perceptron back propagation of the gradient.
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Machine learning for pore-water pressure time-series prediction:Application of recurrent neural networks 认领 引用 被引量：31
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作者 Xin Wei Lulu Zhang +2 位作者 Hao-Qing Yang Limin Zhang Yang-Ping Yao 《Geoscience Frontiers》 SCIE CAS CSCD 2021年第1期453-467,共15页
Knowledge of pore-water pressure(PWP)variation is fundamental for slope stability.A precise prediction of PWP is difficult due to complex physical mechanisms and in situ natural variability.To explore the applicabilit... Knowledge of pore-water pressure(PWP)variation is fundamental for slope stability.A precise prediction of PWP is difficult due to complex physical mechanisms and in situ natural variability.To explore the applicability and advantages of recurrent neural networks(RNNs)on PWP prediction,three variants of RNNs,i.e.,standard RNN,long short-term memory(LSTM)and gated recurrent unit(GRU)are adopted and compared with a traditional static artificial neural network(ANN),i.e.,multi-layer perceptron(MLP).Measurements of rainfall and PWP of representative piezometers from a fully instrumented natural slope in Hong Kong are used to establish the prediction models.The coefficient of determination(R^2)and root mean square error(RMSE)are used for model evaluations.The influence of input time series length on the model performance is investigated.The results reveal that MLP can provide acceptable performance but is not robust.The uncertainty bounds of RMSE of the MLP model range from 0.24 kPa to 1.12 k Pa for the selected two piezometers.The standard RNN can perform better but the robustness is slightly affected when there are significant time lags between PWP changes and rainfall.The GRU and LSTM models can provide more precise and robust predictions than the standard RNN.The effects of the hidden layer structure and the dropout technique are investigated.The single-layer GRU is accurate enough for PWP prediction,whereas a double-layer GRU brings extra time cost with little accuracy improvement.The dropout technique is essential to overfitting prevention and improvement of accuracy. 展开更多
关键词 Pore-water pressure Slope Multi-layer perceptron Recurrent neural networks Long short-term memory Gated recurrent unit
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An Improved SPSA Algorithm for System Identification Using Fuzzy Rules for Training Neural Networks 认领 引用 被引量：2
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作者 Ahmad T.Abdulsadda Kamran Iqbal 《International Journal of Automation and computing》 2011年第3期333-339,共7页
Simultaneous perturbation stochastic approximation （SPSA） belongs to the class of gradient-free optimization methods that extract gradient information from successive objective function evaluation. This paper descri... Simultaneous perturbation stochastic approximation （SPSA） belongs to the class of gradient-free optimization methods that extract gradient information from successive objective function evaluation. This paper describes an improved SPSA algorithm, which entails fuzzy adaptive gain sequences, gradient smoothing, and a step rejection procedure to enhance convergence and stability. The proposed fuzzy adaptive simultaneous perturbation approximation （FASPA） algorithm is particularly well suited to problems involving a large number of parameters such as those encountered in nonlinear system identification using neural networks （NNs）. Accordingly, a multilayer perceptron （MLP） network with popular training algorithms was used to predicate the system response. We found that an MLP trained by FASPSA had the desired accuracy that was comparable to results obtained by traditional system identification algorithms. Simulation results for typical nonlinear systems demonstrate that the proposed NN architecture trained with FASPSA yields improved system identification as measured by reduced time of convergence and a smaller identification error. 展开更多
关键词 Nonlinear system identification simultaneous perturbation stochastic approximation （SPSA） neural networks （NNs）,fuzzy rules multi-layer perceptron （MLP）.
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Determination of penetration depth at high velocity impact using finite element method and artificial neural network tools 认领 引用 被引量：9
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作者 Namlk KILIC Bulent EKICI Selim HARTOMACIOGLU 《Defence Technology（防务技术）》 CAS CSCD 2015年第2期110-122,共13页
Determination of ballistic performance of an armor solution is a complicated task and evolved significantly with the application of finite element methods(FEM)in this research field.The traditional armor design studie... Determination of ballistic performance of an armor solution is a complicated task and evolved significantly with the application of finite element methods(FEM)in this research field.The traditional armor design studies performed with FEM requires sophisticated procedures and intensive computational effort,therefore simpler and accurate numerical approaches are always worthwhile to decrease armor development time.This study aims to apply a hybrid method using FEM simulation and artificial neural network(ANN)analysis to approximate ballistic limit thickness for armor steels.To achieve this objective,a predictive model based on the artificial neural networks is developed to determine ballistic resistance of high hardness armor steels against 7.62 mm armor piercing ammunition.In this methodology,the FEM simulations are used to create training cases for Multilayer Perceptron(MLP)three layer networks.In order to validate FE simulation methodology,ballistic shot tests on 20 mm thickness target were performed according to standard Stanag 4569.Afterwards,the successfully trained ANN(s)is used to predict the ballistic limit thickness of 500 HB high hardness steel armor.Results show that even with limited number of data,FEM-ANN approach can be used to predict ballistic penetration depth with adequate accuracy. 展开更多
关键词 Finite element method(FEM) Artificial neural network(ANN) Multilayer perceptron(MLP) Generalized feed forward(GFF) Ballistics Highhardness armor
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Improvement of Shape Recognition Performance of Sendzimir Mill Control Systems Using Echo State Neural Networks 认领 引用 被引量：2
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作者 Jung-hyun PARK Seong-ik HAN Jong-shik KIM 《Journal of Iron and Steel Research International》 SCIE CAS CSCD 2014年第3期321-327,共7页
High rigidity twenty-high Sendzimir mills （ZRMs） are widely used for rolling stainless steels, silicon sheets, etc. A ZRM uses a small diameter work roll to produce massive rolling forces. Since a work roll with a s... High rigidity twenty-high Sendzimir mills （ZRMs） are widely used for rolling stainless steels, silicon sheets, etc. A ZRM uses a small diameter work roll to produce massive rolling forces. Since a work roll with a small diameter can be bent easily, strips often have complex shapes with mixed quarter and deep edge waves in the shape of plates. In order to solve this problem, fuzzy neural network controls are generally used for shape： recognition in ZRM control systems. Among various neural network types, the multi-layer perceptron （MLP） is typically used in current ZRMs. However, an MLP causes the loss of a large amount of shape recognition data. To improve the shape recognition per- formance of ZRM control systems, echo state networks （ESNs） are proposed to be used. Through simulation re- sults, it is found that shape recognition performance could be improved using the proposed ESN method. 展开更多
关键词 Sendzimir mill neural network multi-layer perceptron echo state network shape recognition
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Comparative Appraisal of Response Surface Methodology and Artificial Neural Network Method for Stabilized Turbulent Confined Jet Diffusion Flames Using Bluff-Body Burners 认领 引用 被引量：1
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作者 Tahani S. Gendy Salwa A. Ghoneim Amal S. Zakhary 《World Journal of Engineering and Technology》 2020年第1期121-143,共23页
The present study was conducted to present the comparative modeling, predictive and generalization abilities of response surface methodology (RSM) and artificial neural network (ANN) for the thermal structure of stabi... The present study was conducted to present the comparative modeling, predictive and generalization abilities of response surface methodology (RSM) and artificial neural network (ANN) for the thermal structure of stabilized confined jet diffusion flames in the presence of different geometries of bluff-body burners. Two stabilizer disc burners tapered at 30° and 60° and another frustum cone of 60°/30° inclination angle were employed all having the same diameter of 80 (mm) acting as flame holders. The measured radial mean temperature profiles of the developed stabilized flames at different normalized axial distances (x/dj) were considered as the model example of the physical process. The RSM and ANN methods analyze the effect of the two operating parameters namely (r), the radial distance from the center line of the flame, and (x/dj) on the measured temperature of the flames, to find the predicted maximum temperature and the corresponding process variables. A three-layered Feed Forward Neural Network in conjugation with the hyperbolic tangent sigmoid (tansig) as transfer function and the optimized topology of 2:10:1 (input neurons: hidden neurons: output neurons) was developed. Also the ANN method has been employed to illustrate such effects in the three and two dimensions and shows the location of the predicted maximum temperature. The results indicated the superiority of ANN in the prediction capability as the ranges of R2 and F Ratio are 0.868 - 0.947 and 231.7 - 864.1 for RSM method compared to 0.964 - 0.987 and 2878.8 7580.7 for ANN method beside lower values for error analysis terms. 展开更多
关键词 Stabilized Turbulent Flames Bluff-Body Burners Thermal Structure Modeling Artificial Neural Network Response Surface Methodology Multi-Layer Perceptron Feed Forward Neural Network
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Performance Comparison of Neural Networks for HRTFs Approximation 认领 引用 被引量：7
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作者 朱晓光 《High Technology Letters》 EI CAS 2000年第1期16-19,共4页
In order to approach to head related transfer functions (HRTFs), this paper employs and compares three kinds of one input neural network models, namely, multi layer perceptron (MLP) networks, radial basis function (RB... In order to approach to head related transfer functions (HRTFs), this paper employs and compares three kinds of one input neural network models, namely, multi layer perceptron (MLP) networks, radial basis function (RBF) networks and wavelet neural networks (WNN) so as to select the best network model for further HRTFs approximation. Experimental results demonstrate that wavelet neural networks are more efficient and useful. 展开更多
关键词 Multi layer perceptron (MLP) Radial basis function (RBF) networks Wavelet neural networks (WNN) Head related transfer functions (HRTFs)
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Applying Neural Network Architecture for Inverse Kinematics Problem in Robotics 认领 引用 被引量：9
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作者 Bassam Daya Shadi Khawandi Mohamed Akoum 《Journal of Software Engineering and Applications》 2010年第3期230-239,共10页
One of the most important problems in robot kinematics and control is, finding the solution of Inverse Kinematics. Inverse kinematics computation has been one of the main problems in robotics research. As the Complexi... One of the most important problems in robot kinematics and control is, finding the solution of Inverse Kinematics. Inverse kinematics computation has been one of the main problems in robotics research. As the Complexity of robot increases, obtaining the inverse kinematics is difficult and computationally expensive. Traditional methods such as geometric, iterative and algebraic are inadequate if the joint structure of the manipulator is more complex. As alternative approaches, neural networks and optimal search methods have been widely used for inverse kinematics modeling and control in robotics This paper proposes neural network architecture that consists of 6 sub-neural networks to solve the inverse kinematics problem for robotics manipulators with 2 or higher degrees of freedom. The neural networks utilized are multi-layered perceptron (MLP) with a back-propagation training algorithm. This approach will reduce the complexity of the algorithm and calculation (matrix inversion) faced when using the Inverse Geometric Models implementation (IGM) in robotics. The obtained results are presented and analyzed in order to prove the efficiency of the proposed approach. 展开更多
关键词 Inverse Geometric Model Neural Network Multi-Layered Perceptron Robotic System Arm
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Identification and Prediction of Internet Traffic Using Artificial Neural Networks 认领 引用 被引量：8
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作者 Samira Chabaa Abdelouhab Zeroual Jilali Antari 《Journal of Intelligent Learning Systems and Applications》 2010年第3期147-155,共9页
This paper presents the development of an artificial neural network (ANN) model based on the multi-layer perceptron (MLP) for analyzing internet traffic data over IP networks. We applied the ANN to analyze a time seri... This paper presents the development of an artificial neural network (ANN) model based on the multi-layer perceptron (MLP) for analyzing internet traffic data over IP networks. We applied the ANN to analyze a time series of measured data for network response evaluation. For this reason, we used the input and output data of an internet traffic over IP networks to identify the ANN model, and we studied the performance of some training algorithms used to estimate the weights of the neuron. The comparison between some training algorithms demonstrates the efficiency and the accu-racy of the Levenberg-Marquardt (LM) and the Resilient back propagation (Rp) algorithms in term of statistical crite-ria. Consequently, the obtained results show that the developed models, using the LM and the Rp algorithms, can successfully be used for analyzing internet traffic over IP networks, and can be applied as an excellent and fundamental tool for the management of the internet traffic at different times. 展开更多
关键词 Artificial Neural Network Multi-Layer Perceptron Training Algorithms Internet Traffic
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Fingerprint Identification by Artificial Neural Network 认领 引用
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作者 Mustapha Boutahri Said El Yamani Samir Zeriouh Abdenabi Bouzid Ahmed Roukhe 《Journal of Physical Science and Application》 2014年第6期381-384,共4页
Biometric techniques require critical operations of digital processing for identification of individuals. In this context, this paper aims to develop a system for automatic processing of fingerprint identification by ... Biometric techniques require critical operations of digital processing for identification of individuals. In this context, this paper aims to develop a system for automatic processing of fingerprint identification by their minutiae using Artificial Neural Networks （ANN）, which reveals to be highly effective. The ANN method implemented is a based on Multi-Layer Perceptron （MLP） model, which utilizes the algorithm of retro-propagation of gradient during the learning process. In such a process, the mean square error generated represents the specific parameter for the identification phase by comparing a fingerprint taken from a crime scene with those of a reference database. 展开更多
关键词 Fingerprint artificial neural network minutiae identification multi-layer perceptron back-propagation of the gradient.
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Prediction of diabetes and hypertension using multi-layer perceptron neural networks 认领 引用 被引量：1
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作者 Hani Bani-Salameh Shadi MAlkhatib +4 位作者 Moawyiah Abdalla Mo’taz Al-Hami Ruaa Banat Hala Zyod Ahed J Alkhatib 《International Journal of Modeling, Simulation, and Scientific Computing》 EI 2021年第2期120-137,共18页
Background:Diabetes and hypertension are two of the commonest diseases in the world.As they unfavorably affect people of different age groups,they have become a cause of concern and must be predicted and diagnosed wel... Background:Diabetes and hypertension are two of the commonest diseases in the world.As they unfavorably affect people of different age groups,they have become a cause of concern and must be predicted and diagnosed well in advance.Objective:This research aims to determine the effectiveness of artificial neural networks(ANNs)in predicting diabetes and blood pressure diseases and to point out the factors which have a high impact on these diseases.Sample:This work used two online datasets which consist of data collected from 768 individuals.We applied neural network algorithms to predict if the individuals have those two diseases based on some factors.Diabetes prediction is based on five factors:age,weight,fat-ratio,glucose,and insulin,while blood pressure prediction is based on six factors:age,weight,fat-ratio,blood pressure,alcohol,and smoking.Method:A model based on the Multi-Layer Perceptron Neural Network(MLP)was implemented.The inputs of the network were the factors for each disease,while the output was the prediction of the disease’s occurrence.The model performance was compared with other classifiers such as Support Vector Machine(SVM)and K-Nearest Neighbors(KNN).We used performance metrics measures to assess the accuracy and performance of MLP.Also,a tool was implemented to help diagnose the diseases and to understand the results.Result:The model predicted the two diseases with correct classification rate(CCR)of 77.6%for diabetes and 68.7%for hypertension.The results indicate that MLP correctly predicts the probability of being diseased or not,and the performance can be significantly increased compared with both SVM and KNN.This shows MLPs effectiveness in early disease prediction. 展开更多
关键词 Artificial Neural Network(ANN) Multi-Layer Perceptron(MLP) SVM KNN decision-making prediction tools diabetes blood pressure hypertension software tools
计及阻尼惯量耦合振荡约束的多虚拟同步机馈入系统多层感知器动态控制策略 认领 引用
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作者 陈艳波 马嘉昊 +2 位作者 刘镇湘 黄涛 张智 《电工技术学报》 EI CSCD 北大核心 2026年第4期1281-1297,共17页
大规模新能源通过变流器接入电网会导致系统惯量降低,频率稳定性面临严峻挑战。采用虚拟同步机(VSG)技术可以使新能源变流器具备同步机组特征,从而改善系统低惯量与低阻尼特性。然而,高比例虚拟同步机的接入将会加剧系统的有功功率振荡... 大规模新能源通过变流器接入电网会导致系统惯量降低,频率稳定性面临严峻挑战。采用虚拟同步机(VSG)技术可以使新能源变流器具备同步机组特征,从而改善系统低惯量与低阻尼特性。然而,高比例虚拟同步机的接入将会加剧系统的有功功率振荡与频率偏差,这是目前VSG馈入系统的主要问题。为此,该文提出一种计及阻尼惯量耦合振荡约束的VSG动态控制策略。首先,建立多机并联系统功率传输方程频域表达模型,推导并分析多机并联系统阻尼惯量耦合振荡特性,量化多机参数耦合下的振荡约束条件;其次,综合联立单机动态调整特性与多机耦合约束,确立各机组虚拟惯量、阻尼系数的动态约束区间,并设计基于多层感知器(MLP)神经网络的动态控制模型,实现各机组参数的解耦控制;最后,通过算例进行了分析,验证了该文所设计动态控制策略对有功振荡与频率偏差具有良好的抑制效果。 展开更多
关键词 虚拟同步机 多机系统 阻尼惯量耦合 多层感知器(MLP)神经网络 动态控制
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基于MLP改进型深度神经网络学习资源推荐算法 认领 引用 被引量：25
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作者 樊海玮 史双 +3 位作者 张博敏 张艳萍 蔺琪 孙欢 《计算机应用研究》 CSCD 北大核心 2020年第9期2629-2633,共5页
针对在线学习过程中出现的知识过载及传统推荐算法中存在的数据稀疏和冷启动问题,提出了一种基于多层感知机(MLP)的改进型深度神经网络学习资源推荐算法。该算法利用多层感知机对非线性数据处理的优势,将学习者特征和学习资源特征进行... 针对在线学习过程中出现的知识过载及传统推荐算法中存在的数据稀疏和冷启动问题,提出了一种基于多层感知机(MLP)的改进型深度神经网络学习资源推荐算法。该算法利用多层感知机对非线性数据处理的优势,将学习者特征和学习资源特征进行向量相乘的预测方式转换为输入多层感知机的方式,改进了DN-CBR神经网络推荐模型。为验证模型的有效性,以爱课程在线学习平台数据为样本构建数据集,通过对比实验表明,在该数据集上,改进后模型相较于DN-CBR模型在归一化折损累积增益和命中率指标上分别提升了1.2%和3%,有效地提高了模型的推荐性能。 展开更多
关键词 学习资源推荐 深度学习 卷积神经网络 word2vec 多层感知机
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用于短文本分类的BLSTM_MLPCNN模型 认领 引用 被引量：12
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作者 郑诚 洪彤彤 薛满意 《计算机科学》 CSCD 北大核心 2019年第6期206-211,共6页
文本表示和文本特征提取是自然语言处理的基础工作,直接影响文本分类的性能。文中提出了以字符级向量联合词向量作为输入的BLSTM_MLPCNN神经网络模型。该模型首先将卷积神经网络(CNN)作用于字符以获取字符级向量,并将字符级向量联合词... 文本表示和文本特征提取是自然语言处理的基础工作,直接影响文本分类的性能。文中提出了以字符级向量联合词向量作为输入的BLSTM_MLPCNN神经网络模型。该模型首先将卷积神经网络(CNN)作用于字符以获取字符级向量,并将字符级向量联合词向量作为预训练词嵌入向量,也即双向长短时记忆网(BLSTM)模型的输入;然后联合BLSTM模型的前向输出、词嵌入向量、后向输出构成文档特征图;最后利用多层感知器卷积神经网络(MLPCNN)进行特征提取。在相关数据集上的实验结果表明:相比于CNN,RNN以及CNN与RNN的组合模型,BLSTM_MLPCNN模型具有更优的分类性能。 展开更多
关键词 字符级向量 词向量 卷积神经网络(CNN) 双向长短时记忆神经网络(BLSTM) 多层感知器(MLP) 多层感知器卷积网络(MLPCNN)
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SSA-MLP模型在岩质边坡稳定性预测中的应用 认领 引用 被引量：14
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作者 侯克鹏 包广拓 孙华芬 《安全与环境学报》 CAS CSCD 北大核心 2024年第5期1795-1803,共9页
岩质边坡的力学参数量化及稳定性分析对岩质边坡灾害的防治具有重要意义。Hoek-Brown(H B)准则是一种用于确定岩体力学参数的经典方法,能反映出边坡岩体变形和位移的非线性破坏特征。在此基础上,首先,提出一种麻雀搜索算法(Sparrow Sear... 岩质边坡的力学参数量化及稳定性分析对岩质边坡灾害的防治具有重要意义。Hoek-Brown(H B)准则是一种用于确定岩体力学参数的经典方法,能反映出边坡岩体变形和位移的非线性破坏特征。在此基础上,首先,提出一种麻雀搜索算法(Sparrow Search Algorithm,SSA)改进多层感知器(Multi-Layer Perceptron,MLP)的神经网络模型,并用于边坡稳定性预测、指标敏感性分析及参数反演。其次,将收集的1085组岩质边坡的几何参数和H B准则参数等作为输入变量,极限平衡理论Bishop法求解的安全系数作为输出变量,对SSA MLP模型进行训练学习和性能评估。最后,将该模型运用于25个边坡实例,验证模型的有效性。结果显示,该模型收敛速度快、精度高,为边坡稳定性分析和参数量化提供了一种新思路。 展开更多
关键词 安全工程 边坡稳定性 Hoek-Brown准则 多层感知器(MLP)神经网络 麻雀搜索算法 参数反演
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