A new design of evolutionary hybrid optimization of SVR model in predicting the blast-induced ground vibration

This study aims to identify the suitability of hybridizing the firefly algorithm (FA), genetic algorithm (GA), and particle swarm optimization (PSO) with two well-known data-driven models of support vector regression (SVR) and artificial neural network (ANN) to predict blast-induced ground vibration. Here, these combinations are abbreviated using FA–SVR, PSO–SVR, GA–SVR, FA–ANN, PSO–ANN, and GA–ANN models. In addition, a modified FA (MFA) combined with SVR model is also proposed in this study, namely, MFA–SVR. The feasibility of the proposed models is examined using a case study, located in Johor, Malaysia. Then, to provide an objective assessment of performances of the predictive models, their results were compared based on several well known and popular statistical criteria. According to the results, the MFA–SVR with the coefficient of determination (R²) of 0.984 and root mean square error (RMSE) of 0.614 was more accurate model to predict PPV than the PSO–SVR with R² = 0.977 and RMSE = 0.725, the FA–SVR with R² = 0.964 and RMSE = 0.923, the GA–SVR with R² = 0.957 and RMSE = 1.016, the GA–ANN with R² = 0.936 and RMSE = 1.252, the FA–ANN with R² = 0.925 and RMSE = 1.368, and the PSO–ANN with R² = 0.924 and RMSE = 1.366. Consequently, the MFA–SVR model can be sufficiently employed in estimating the ground vibration, and has the capacity to generalize.

     

Weather analysis using ensemble of connectionist learning paradigms

This paper presents a comparative analysis of different connectionist and statistical models for forecasting the weather of Vancouver, Canada. For developing the models, one year's data comprising of daily temperature and wind speed were used. A multi-layered perceptron network (MLPN) and an Elman recurrent neural network (ERNN) were trained using the one-step-secant and Levenberg–Marquardt algorithm. Radial basis function network (RBFN) was employed as an alternative to examine its applicability for weather forecasting. To ensure the effectiveness of neurocomputing techniques, the connectionist models were trained and tested using different datasets. Moreover, ensembles of the neural networks were generated by combining the MLPN, ERNN and RBFN using arithmetic mean and weighted average methods. Subsequently, performance of the connectionist models and their ensembles were compared with a well-established statistical technique. Experimental results obtained have shown RBFN produced the most accurate forecast model compared to ERNN and MLPN. Overall, the proposed ensemble approach produced the most accurate forecast, while the statistical model was relatively less accurate for the weather forecasting problem considered.     

Flank wear prediction in drilling using back propagation neural network and radial basis function network

In the present work, two different types of artificial neural network (ANN) architectures viz. back propagation neural network (BPNN) and radial basis function network (RBFN) have been used in an attempt to predict flank wear in drills. Flank wear in drill depends upon speed, feed rate, drill diameter and hence these parameters along with other derived parameters such as thrust force, torque and vibration have been used to predict flank wear using ANN. Effect of using increasing number of sensors in the efficacy of predicting drill wear by using ANN has been studied. It has been observed that inclusion of vibration signal along with thrust force and torque leads to better prediction of drill wear. The results obtained from the two different ANN architectures have been compared and some useful conclusions have been made.     

A new combination of artificial neural network and <Emphasis Type="Italic">K</Emphasis>-nearest neighbors models to predict blast-induced ground vibration and air-overpressure

Blasting operation is widely used method for rock excavation in mining and civil works. Ground vibration and air-overpressure (AOp) are two of the most detrimental effects induced by blasting. So, evaluation and prediction of ground vibration and AOp are essential. This paper presents a new combination of artificial neural network (ANN) and K-nearest neighbors (KNN) models to predict blast-induced ground vibration and AOp. Here, this combination is abbreviated using ANN-KNN. To indicate performance of the ANN-KNN model in predicting ground vibration and AOp, a pre-developed ANN as well as two empirical equations, presented by United States Bureau of Mines (USBM), were developed. To construct the mentioned models, maximum charge per delay (MC) and distance between blast face and monitoring station (D) were set as input parameters, whereas AOp and peak particle velocity (PPV), as a vibration index, were considered as output parameters. A database consisting of 75 datasets, obtained from the Shur river dam, Iran, was utilized to develop the mentioned models. In terms of using three performance indices, namely coefficient correlation (R ²), root mean square error and variance account for, the superiority of the ANN-KNN model was proved in comparison with the ANN and USBM equations.     

Investigating the effective parameters on the risk levels of rockburst phenomena by developing a hybrid heuristic algorithm

When working on underground projects, especially where ground is burst prone, it is of a high significance to accurately predict the risk of rockburst. The present paper integrates the firefly algorithm (FA) and artificial neural network (ANN) aiming at modeling the complex relationship between the rockburst risk in deep mines and tunnels and factors effective on this phenomenon. The model was established and validated through the use of a data set extracted from previously conducted studies. The data set involves a total of 196 reliable rockburst cases. The use of smart systems was used to classify and determine patterns in this research using model development. The hybrid FA–ANN model provides a solution for determining different classes of hazard under different conditions. The capability of these developed systems was implemented to determine the four types of levels defined for this phenomenon. The results of these systems led to new solutions to classify this phenomenon by success rates. Each system, given its performance, yields a unique error. Finally, by combining the number of correctly classified classes and their error values, the success rates in the classification of rockburst phenomena in mines and underground tunnels were evaluated.

     

支持向量机与神经网络相结合的板带凸度预测

为提高热轧生产过程中板带凸度的预测精度,提出了一种将粒子群优化算法(particle swarm optimization, PSO)、支持向量回归(support vector regression, SVR)和BP神经网络(back propagation neural network, BPNN)相结合的板带凸度预测模型。采用PSO算法优化SVR模型的参数,建立了PSO-SVR板带凸度预测模型,提出采用BPNN建立板带凸度偏差模型与PSO-SVR板带凸度模型相结合的方法对板带凸度进行预测。采用现场数据对模型的预测精度进行验证,并采用统计指标评价模型的综合性能。仿真结果表明,与PSO-SVR、SVR、BPNN和GA-SVR模型进行比较,PSO-SVR+BPNN模型具有较高的学习能力和泛化能力,并且比GA-SVR模型运算时间短。     

Comparative study of cognitive systems for ground vibration measurements

This paper deals with the application of a support vector machine (SVM) optimization technique to predict the blast-induced ground vibration. Peak particle velocity (PPV) is an important parameter to be kept under control to minimize the damage caused due to the ground vibration. A number of previous researchers have tried to use different empirical methods to predict PPV, but these empirical equations have their limitations due to its less versatile application and acceptability from field conditions. Therefore, it is difficult to apply these empirical equations to predict PPV because they are based on limited parameters which does not really reflect and connect with real influencing parameters. In this paper, SVM technique is used for the prediction of PPV by incorporating blast design and explosive parameters, and the suitability of one technique over other has been tested based on the results. To avoid the biasness in man-made choice of parameters of SVM, we have used the chaos optimization algorithm to find the optimal parameters which can help the model to enhance the learning efficiency and capability of prediction. Datasets have been obtained from one of the large opencast mine from southeastern coalfield limited, Chhattisgarh, India. One hundred and twenty-seven datasets were used to establish SVM architecture, and 10 datasets have been randomly chosen for validation of SVM model to see its prediction potential. The results obtained have been compared with different vibration predictors, multivariate regression analysis, artificial neural network and the superiority of application on SVM over previous methodology. The mean absolute percentage error using SVM is very low (0.001) as compared to other predictors indicate its better prediction capability.

     

An optimized ANN model based on genetic algorithm for predicting ripping production

Due to the environmental constraints and the limitations on blasting, ripping as a ground loosening and breaking method has become more popular in both mining and civil engineering applications. As a result, a more applicable rippability model is required to predict ripping production (Q) before conducting such tests. In this research, a hybrid genetic algorithm (GA) optimized by artificial neural network (ANN) was developed to predict ripping production results obtained from three sites in Johor state, Malaysia. It should be noted that the mentioned hybrid model was first time applied in this field. In this regard, 74 ripping tests were investigated in the studied areas and the relevant parameters were also measured. A series of GA–ANN models were conducted in order to propose a hybrid model with a higher accuracy level. To demonstrate the performance capacity of the hybrid GA–ANN model, a pre-developed ANN model was also proposed and results of predictive models were compared using several performance indices. The results revealed higher accuracy of the proposed hybrid GA–ANN model in estimating Q compared to ANN technique. As an example, root-mean-square error values of 0.092 and 0.131 for testing datasets of GA–ANN and ANN techniques, respectively, express the superiority of the newly developed model in predicting ripping production.

     

A new approach to predict normalized difference vegetation index using time-delay neural network in the arid and semi-arid grassland

ABSTRACT

Normalized difference vegetation index (NDVI) has been used to conduct important research on plant growth and vegetation productivity. In this paper, a new approach to predict NDVI based on precipitation in the grass-growing season for the arid and semi-arid grassland is proposed using time-delay neural network (TDNN). To intuitively know the ability of TDNN to learn the relationship between NDVI and precipitation and to predict NDVI, the performance of the TDNN model is compared with back propagation neural network (BPNN) trained with the same data. The results indicate that TDNN works well to predict precipitation. Moreover, the relationship between precipitation and NDVI can be predicted accurately by the proposed TDNN model. The results show the goodness-of-fit between the observed NDVI and predicted NDVI measured by the determination coefficient of R² being 0.999 from the TDNN model, with the mean absolute percentage error, mean absolute error, and root-mean-square error to be 0.23%, 0.20, and 0.19, respectively. The analysis shows that the proposed method can result in an accurate NDVI prediction. Thus, the algorithm is applied to predict the NDVI during the grass-growing season for the validation of the approach. This validation results suggest the potential application of this approach for reduction of overgrazing pressure and vegetation restoration in the arid and semi-arid grassland.     

Estimation of formation strength index of aquifer from neural networks

The purpose of this study is to construct a model that predicts an aquifer's formation strength index (the ratio of shear modulus and bulk compressibility, G/C_b) from geophysical well logs by using a back-propagation neural network (BPNN). The BPNN model of an aquifer's formation strength index is developed using a set of well logging data. The model is a [4-5-1] three-layer BPNN with a four-neuron input layer (depth, gamma-ray log data, formation density log data, and sonic log data, respectively), a five-neuron hidden layer, and a one-neuron output layer (formation strength index).The optimal learning rate and momentum constant used in the BPNN model are obtained from serial combinative experiments. The inside test and outside test are implemented to check the performance of network learning and the prediction ability of the network, respectively. The results of the inside test, based on 84 training data sets from a total of 105 data sets, show that the network has been well-trained because the mean square error between the network output value and the target value from the inside test is very small (1.1×10⁻⁴). The results of the outside test, based on 21 testing data sets from 105 data sets, show the excellent prediction ability of the BPNN model, because the network prediction values closely track with the target values (the mean square error is 2.1×10⁻⁴).     

Neural network models for group behavior prediction: a case of soccer match attendance

Soccer match attendance is an example of group behavior with noisy context that can only be approximated by a limited set of quantifiable factors. However, match attendance is representative of a wider spectrum of context-based behaviors for which only the aggregate effect of otherwise individual decisions is observable. Modeling of such behaviors is desirable from the perspective of economics, psychology, and other social studies with prospective use in simulators, games, product planning, and advertising. In this paper, we evaluate the efficiency of different neural network architectures as models of context in attendance behavior by comparing the achieved prediction accuracy of a multilayer perceptron (MLP), an Elman recurrent neural network (RNN), a time-lagged feedforward neural network (TLFN), and a radial basis function network (RBFN) against a multiple linear regression model, an autoregressive moving average model with exogenous inputs, and a naive cumulative mean model. We show that the MLP, TLFN, and RNN are superior to the RBFN and achieve comparable prediction accuracy on datasets of three teams from the English Football League Championship, which indicates weak importance of context transition modeled by the TLFN and the RNN. The experiments demonstrate that all neural network models outperform linear predictors by a significant margin. We show that neural models built on individual datasets achieve better performance than a generalized neural model constructed from pooled data. We analyze the input parameter influences extracted from trained networks and show that there is an agreement between nonlinear and linear measures about the most significant attributes.

     

Genetically evolved radial basis function network based prediction of drill flank wear

The most important factor that governs the performance of a radial basis function network (RBFN) is the optimization of the network architecture, i.e. determining the exact number of radial basis functions (RBFs) in the hidden layer that can best minimize the error between the actual and network outputs. This work presents a genetic algorithm (GA) based evolution of optimal RBFN architecture and compares its performance with the conventional RBFN training procedure employing a two stage methodology, i.e. utilizing the k-means clustering algorithm for the unsupervised training in the first stage, and using linear supervised techniques for subsequent error minimization in the second stage. The validation of the proposed methodology is carried out for the prediction of flank wear in the drilling process following a series of experiments involving high speed steel (HSS) drills for drilling holes on mild-steel workpieces. The genetically grown RBFN not only provides an improved network performance, it is also computationally efficient as it eliminates the need for the error minimization routine in the second stage training of RBFN.     

Prediction of seismic slope stability through combination of particle swarm optimization and neural network

One of the main concerns in geotechnical engineering is slope stability prediction during the earthquake. In this study, two intelligent systems namely artificial neural network (ANN) and particle swarm optimization (PSO)–ANN models were developed to predict factor of safety (FOS) of homogeneous slopes. Geostudio program based on limit equilibrium method was utilized to obtain 699 FOS values with different conditions. The most influential factors on FOS such as slope height, gradient, cohesion, friction angle and peak ground acceleration were considered as model inputs in the present study. A series of sensitivity analyses were performed in modeling procedures of both intelligent systems. All 699 datasets were randomly selected to 5 different datasets based on training and testing. Considering some model performance indices, i.e., root mean square error, coefficient of determination (R ²) and value account for (VAF) and using simple ranking method, the best ANN and PSO–ANN models were selected. It was found that the PSO–ANN technique can predict FOS with higher performance capacities compared to ANN. R ² values of testing datasets equal to 0.915 and 0.986 for ANN and PSO–ANN techniques, respectively, suggest the superiority of the PSO–ANN technique.     

Neural network-based adaptive multiuser detection schemes in SDMA–OFDM system for wireless application

Neural network applications in adaptive multiuser detection (MUD) schemes are suggested here in the context of space division multiple access–orthogonal frequency division multiplexing system. In this paper, various neural network (NN) models like feed forward network (FFN), recurrent neural network (RNN) and radial basis function network (RBFN) are adopted for MUD. MUD using NN models outperforms other existing schemes like genetic algorithm--assisted minimum bit error rate (MBER) and minimum mean square error MUDs in terms of BER performance and convergence speed. Among these NN models, the FNN MUD performs efficiently as RNN in full load scenario, where the number of users is equal to number of receiving antennas. In overload scenario, where the number of users is more than the number of receiving antennas, the FNN MUD performs better than RNN MUD. Further, the RBFN MUD provides a significant enhancement in performance over FNN and RNN MUDs under both overload and full load scenarios because of its better classification ability due to Gaussian nonlinearity. Extensive simulation analysis considering Stanford University Interim channel models applied for fixed wireless applications shows improvement in convergence speed and BER performance of the proposed NN-based MUD algorithms.     

Artificial neural networks for infectious diarrhea prediction using meteorological factors in Shanghai (China)

Infectious diarrhea is an important public health problem around the world. Meteorological factors have been strongly linked to the incidence of infectious diarrhea. Therefore, accurately forecast the number of infectious diarrhea under the effect of meteorological factors is critical to control efforts. In recent decades, development of artificial neural network (ANN) models, as predictors for infectious diseases, have created a great change in infectious disease predictions. In this paper, a three layered feed-forward back-propagation ANN (BPNN) model trained by Levenberg–Marquardt algorithm was developed to predict the weekly number of infectious diarrhea by using meteorological factors as input variable. The meteorological factors were chosen based on the strongly relativity with infectious diarrhea. Also, as a comparison study, the support vector regression (SVR), random forests regression (RFR) and multivariate linear regression (MLR) also were applied as prediction models using the same dataset in addition to BPNN model. The 5-fold cross validation technique was used to avoid the problem of overfitting in models training period. Further, since one of the drawbacks of ANN models is the interpretation of the final model in terms of the relative importance of input variables, a sensitivity analysis is performed to determine the parametric influence on the model outputs. The simulation results obtained from the BPNN confirms the feasibility of this model in terms of applicability and shows better agreement with the actual data, compared to those from the SVR, RFR and MLR models. The BPNN model, described in this paper, is an efficient quantitative tool to evaluate and predict the infectious diarrhea using meteorological factors.     

Evaluation of the performance of backpropagation and radial basis function neural networks in predicting the drill flank wear

This study compares the performance of backpropagation neural network (BPNN) and radial basis function network (RBFN) in predicting the flank wear of high speed steel drill bits for drilling holes on mild steel and copper work pieces. The validation of the methodology is carried out following a series of experiments performed over a wide range of cutting conditions in which the effect of various process parameters, such as drill diameter, feed-rate, spindle speed, etc. on drill wear has been considered. Subsequently, the data, divided suitably into training and testing samples, have been used to effectively train both the backpropagation and radial basis function neural networks, and the individual performance of the two networks is then analyzed. It is observed that the performance of the RBFN fails to match that of the BPNN when the network complexity and the amount of data available are the constraining factors. However, when a simpler training procedure and reduced computational times are required, then RBFN is the preferred choice.     

A new technique to predict fly-rock in bench blasting based on an ensemble of support vector regression and GLMNET

Fly-rock caused by blasting is one of the dangerous side effects that need to be accurately predicted in open-pit mines. This study proposed a new technique to predict the distance of fly-rock based on an ensemble of support vector regression models (SVRs) and Lasso and elastic-net regularized generalized linear model (GLMNET), called SVRs–GLMNET. It was developed based on a combination of six SVR models and a GLMNET model. Accordingly, the dataset including 210 experimental data was divided into three parts, i.e., training, validating, and testing. Of the whole dataset, 70% was used for the development of the six SVR models first as the sub-models. Subsequently, 20% of the entire dataset (the validating dataset) was used to predict fly-rock based on the six developed SVR models. The predicted results from the six developed SVR models were used as the input variables to establish the GLMNET model (i.e., SVRs–GLMNET model). Finally, the remaining 10% of the dataset was used for testing the performance of the proposed SVRs–GLMNET model. A comparison and evaluation of the six developed SVR models and the proposed SVRs–GLMNET model were implemented based on five statistical criteria, such as mean absolute error (MAE), mean absolute percentage error (MAPE), root-mean-square error (RMSE), variance account for (VAF), and determination of correlation (R²). The results indicated that the proposed SVRs–GLMNET model provided the most dominant performance in predicting the distance of fly-rock caused by bench blasting in this study with an RMSE of 3.737, R² of 0.993, MAE of 3.214, MAPE of 0.018, and VAF of 99.207. Whereas, the other models yielded poorer accuracy with RMSE of 7.058–12.779, R² of 0.920–0.972, MAE of 3.438–7.848, MAPE of 0.021–0.055, and VAF of 90.538–97.003.

     

基于深度学习的城市轨道交通短时客流量预测

我国城市轨道交通已进入快速发展期,准确预测城轨交通短时客流量,对于城轨运营安全、运营效率及运营成本具有重要意义.城轨交通短时客流量由于具有强随机性、周期性、相关性及非线性的特征,浅层模型的预测精度并不理想.对此,基于深度信念网络(DBN)和支持向量回归机(SVM),提出城轨交通短时客流深层预测模型(DBN-P/GSVM),同时基于遗传算法(GA)和粒子群算法(PSO)实现SVM的参数寻优.最后,对成都地铁火车北站客流量预测进行实例分析.结果表明,DBN-P/GSVM深度预测模型在均方误差、均方根误差、绝对误差均值及绝对百分比误差均值等方面均优于浅层模型——GA-SVM模型、PSO-SVM模型和BP神经网络模型,以及深层模型长短期记忆网络(LSTM)与LSTM-Softmax.     

Use of neural network to model X-ray photoelectron spectroscopy data for diagnosis of plasma etch equipment

X-ray photoelectron spectroscopy (XPS) is widely used to analyze chemical states on plasma-processed film surfaces. For XPS data to be utilized for plasma monitoring, a prediction model is in demand. This type of model can further be used for optimization of chemical compositions on plasma processes film surfaces. In this study, a prediction model is constructed by combining XPS and backpropagation neural network (BPNN). Genetic algorithm (GA) was used to improve the BPNN prediction performance. The experimental data were collected during the plasma etching of silicon carbide films in a NH₄–CF₄ inductively coupled plasma. For a systematic modeling, the etching process was characterized by means of face-centered Box Wilson experiment. Four major peaks modeled include C1s, O1s, N1s, and Si2p. For comparisons, other conventional and statistical models were also constructed. For all peaks, GA-BPNN models yielded an improved prediction with respect to conventional BPNN and statistical regression models. The improvements were even more than 30% over BPNN models for C1s and Si2p and over statistical regression models for O1s and Si2p.     

Ｓｔｅｗａｒｔ主动隔振平台的神经网络自适应控制

针对Stewart主动隔振平台,提出一种基于径向基函数(RBF)神经网络的多输入多输出自适应隔振控制方法.考虑外界振动对Stewart主动隔振平台动态特性的影响,建立了隔振平台在工作空间中的动力学模型.推导出RBF神经网络的权值矩阵、高斯基函数中心和宽度的在线自适应调节律,以使神经网络快速逼近系统的非线性动态函数.应用Lyapunov稳定性理论,证明了在扰动力和神经网络逼近误差有界的条件下,闭环控制系统滤波误差和RBF神经网络各调节参数估计误差的一致最终有界.仿真结果表明,该控制方法能有效地抑制不同方向的低频有界振动.