SSA-MLP模型在岩质边坡稳定性预测中的应用

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。

Design an Artificial Neural Network by MLP Method;Analysis of the Relationship between Demographic Variables, Resilience, COVID-19 and Burnout

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.

Machine learning for pore-water pressure time-series prediction:Application of recurrent neural networks

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.

Performance Comparison of Neural Networks for HRTFs Approximation

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Identification and Prediction of Internet Traffic Using Artificial Neural Networks

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.

An Improved SPSA Algorithm for System Identification Using Fuzzy Rules for Training Neural Networks

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.

Applying Neural Network Architecture for Inverse Kinematics Problem in Robotics

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.

Determination of penetration depth at high velocity impact using finite element method and artificial neural network tools

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.

Comparative Appraisal of Response Surface Methodology and Artificial Neural Network Method for Stabilized Turbulent Confined Jet Diffusion Flames Using Bluff-Body Burners

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.

Prediction of diabetes and hypertension using multi-layer perceptron neural networks

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.

用于短文本分类的BLSTM_MLPCNN模型

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。

基于MLP改进型深度神经网络学习资源推荐算法

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。

基于MLP-ANN和SVM方法的多氯代二苯并呋喃光解半衰期QSPR比较研究

多氯代二苯并呋喃(PCDFs)是全球性污染物之一,光化学降解是其主要的环境降解途径。基于分子二维拓扑结构提出的用于表征化合物结构参数的分子电性距离矢量描述子(MEDV),应用多层感知器神经网络(MLP-ANN)和支持向量机(SVM)对PCDFs在云杉针叶和飞灰表面的光解半衰期(t1/2)进行定量结构-性质相关(QSPR)分析,并用交互检验和外部样本对所建模型的稳定性进行了检验。旨在为PCDFs光解机理的QSPR研究提供新思路。结果表明,所建模型均具有良好的稳定性和预测能力,尤以MLP-ANN模型为佳,其建模相关系数(Rcum)、留一法交互检验相关系数(Q LOO)以及外部样本检验相关系数(Q ext)分别为0.850、0.816、0.954(云杉针叶表面)和0.892、0.753、0.897(飞灰表面)。

线性化逐层优化MLP训练算法

提出了线性化逐层优化ＭＬＰ训练算法（ＬＯＬＬ）．ＬＯＬＬ采用循环方式逐层对ＭＬＰ的连接权值进行训练．训练连接权值时用一阶泰勒级数表示神经元的非线性激活函数以实现神经网络的线性化，使ＭＬＰ的训练问题转化为一个线性问题．同时，为保证神经网络线性化条件不被破坏，ＬＯＬＬ通过在神经网络的误差函数中计入部分线性化误差限制参数的改变幅度，对神经网络的误差函数进行了修正．实验结果显示，ＬＯＬＬ训练算法的速度比传统的ＢＰ算法快４倍，用它构成的语音信号非线性预测器有较好的预测性能．

一种基于MLP神经网络的大额损失飞行事故预测模型

运用多层感知器(MLP)神经网络方法构建了大额损失飞行事故的预测模型,并利用CASE数据库中抽取的飞行事故案例进行了检验.预测效果检验表明,所构建的模型具有较好的拟合程度和预测效果.机身价值和机龄是大额损失飞行事故的重要影响因素.

基于VMD与MLP的电机轴承故障检测方法

针对现有的永磁同步电机(PMSM)轴承故障检测方法准确度低的问题,对PMSM轴承故障表征方法和基于神经网络的检测方法进行了研究,提出了一种PMSM轴承故障归一化表征指标集合的构建方法,和一种基于VMD和MLP的PMSM轴承故障检测方法。首先,采用融合PMSM轴承故障频域特征进行归一化处理的方法,构建了一个PMSM轴承故障表征指标集合;然后,利用优化后的变分模态分解(VMD)方法,对振动信号进行了降噪重构,提取了故障频域特征,并计算出了归一化指标集合;利用基于多层感知器(MLP)的神经网络模型对获取的归一化指标集合进行了训练,得到了一种高准确度PMSM轴承故障检测器;最后,采用了一套可以模拟数控机床进给传动系统的试验测试装置,对基于VMD和MLP的PMSM轴承故障检测方法的有效性和先进性进行了验证。研究结果表明:PMSM轴承故障表征指标集合比现有的指标具有更强的故障表征能力,基于VMD和MLP的PMSM检测方法的平均检测准确度高达95.4%;该结果验证了归一化PMSM轴承故障表征指标集合的先进性,以及基于VMD与MLP的PMSM轴承故障检测方法的有效性。

基于MLP神经网络模型的客户评分应用研究

判断客户对产品购买的可能性,是企业营销人员重点关注的问题。针对保险产品客户与其他金融客户交叉销售,采用人工智能方法高精度量化客户的潜在购买力。根据对个人保险客户营销的总结,提出保险客户购买评分模型。通过使用中国邮政代理金融的简易保险客户数据,对模型的有效性进行实证研究。研究结果表明,该模型提供了较高效的预测准确率和具体的评价标准,具有良好的预测功能,可以帮助企业及时发现最有效的营销客户,最大程度上提高营销成功率。

Voice to Face Recognition Using Spectral ERB-DMLP Algorithms

Designing an authentication system for securing the power plants are important to allow only specific staffs of the power plant to access the certain blocks so that they can be restricted from using high risk-oriented equipment.This authentication is also vital to prevent any security threats or risks like compromises of business server,release of confidential data etc.Though conventional works attempted to accomplish better authentication,they lacked with respect to accuracy.Hence,the study aims to enhance the recognition rate by introducing a voice recognition system as a personal authentication based on Deep Learning(DL)due to its ability to perform effective learning.The study proposes Equivalent Rectangular Bandwidth and Deep Multi-Layer Perceptron(ERB-DMLP)as it has the ability to perform efficient and relevant feature extraction and faster classification.This algorithm also has the ability to establish effective correlation between voices and images and achieve the semantic relationship between them.Voice preprocessing is initially performed to make it suitable for further processing by removing the noise and enhancing the quality of signal.This process is also vital to minimize the extra computations so that the overall efficacy of the system can be made flexible by considering the audio files as features and the images as labels to identify a person’s voice by classifying the extracted features from the ERB Feature Extraction.This is then passed as the input into DMLP model to classify the persons,and trained the model to make an accurate classification of audio with corresponding image labels,and perform the performance test based on the trained model.Flexibility,relevant feature extraction and faster classification ability of the proposed work has made it explore better outcomes that is confirmed through results.

Sensitivity Analysis of Radial Basis Function Networks for River Stage Forecasting

Sensitivity analysis of neural networks to input variation is an important research area as it goes some way to addressing the criticisms of their black-box behaviour. Such analysis of RBFNs for hydrological modelling has previously been limited to exploring perturbations to both inputs and connecting weights. In this paper, the backward chaining rule that has been used for sensitivity analysis of MLPs, is applied to RBFNs and it is shown how such analysis can provide insight into physical relationships. A trigonometric example is first presented to show the effectiveness and accuracy of this approach for first order derivatives alongside a comparison of the results with an equivalent MLP. The paper presents a real-world application in the modelling of river stage shows the importance of such approaches helping to justify and select such models.

Integrating the effect of abutments in estimating the average vertical stress of elastic hard rock pillars by combining numerical modelling and artificial neural networks

Estimating average vertical pillar stresses is a critical step in designing room-and-pillar mines.Several analytical methods can be used to estimate the vertical stresses acting on the pillars.However,the present analytical methods fail to adequately account for the influence of abutments on the distribution of vertical stresses,especially when applied to narrow panel widths and pillar layouts comprising evenly spaced barriers.In this study,a multi-layer perceptron neural network(MLPNN)was applied to predict the vertical loads of regular pillars more accurately.Hundreds of room-and-pillar mine layouts were modeled using a displacement discontinuity method(DDM),and a database of 2355 sampled pillar cases was compiled.The MLPNN was trained based on this database,and its prediction capabilities were further validated using simulations by a finite difference code(i.e.,FLAC3D).The model predictions and the FLAC3D simulations reasonably agreed with a regression coefficient of 0.99.The model was also adapted for mine cases with evenly spaced barrier pillars,and its application to a real case study mine has shown to provide accurate pillar stress estimations;hence,this model is suitable for practical use at mines.Even though the MLPNN model cannot be applied universally to all mine situations,it seems as a significant improvement over existing analytical techniques in terms of accounting for the influence of abutments on pillar stresses.