A neural network approach for prediction of discharge in straight compound open channel flow

Most natural rivers and streams consist of two stage channels known as main channel and flood plains. Accurate prediction of discharge in compound open channels is extremely important from river engineering point of view. It helps the practitioners to provide essential information regarding flood mitigation, construction of hydraulic structures and prediction of sediment load so as to plan for effective preventive measures. Discharge determination models such as the single channel method (SCM), the divided channel method (DCM), the coherence method (COHM) and the exchange discharge method (EDM) are widely used; however, they are insufficient to predict discharge accurately. Therefore, an attempt has been made in this work to predict the total discharge in compound channels with an artificial neural network (ANN) and compare with the above models. The mean absolute percentage error with artificial neural networks is found to be consistently low as compared to other models.     

Discharge coefficient of rectangular sharp-crested side weirs,Part I: Traditional weir equation

A comprehensive study was performed to examine the flow characteristics over rectangular sharp-crested side weirs based on the traditional weir equation. To obtain a generally convenient discharge coefficient relationship, series of experiments were conducted according to manipulation of different prevailing parameters. The flow regime was consistently subcritical for upstream Froude numbers ranging from 0.08 to 0.91. Furthermore, experimental data sets of the former investigators were also applied. In order to identify the most important parameters affecting the discharge coefficient of rectangular sharp-crested side weirs, a sensitivity analysis was carried out based upon an artificial neural network modeling. Results of the sensitivity analysis indicated the Froude number to be the most influential parameter on discharge coefficient. Accordingly, a power equation is derived for estimating the discharge coefficient, which is applicable for both sub- and supercritical flow conditions simultaneously. Moreover, considering all the influential parameters, a nonlinear correlation was obtained with the highest precision to determine the discharge coefficient of sharp-crested rectangular side weirs.     

Estimating the uncertainty of discharge coefficient predicted for oblique side weir using Monte Carlo method

Side weir is a hydraulic structure, which is used in irrigation systems to divert some water from main to side channel. It is installed at the entrance of the side channel to control and measure passing water into the side channel. Many studies provided side weir water surface profile and coefficient of discharge to measure water discharge diverted into the side channel. These studies dealt with different side weir shapes (rectangular, trapezoidal, triangular and circular), which were installed perpendicular to the flow direction. Recently, some studies dealt with skew side weir, but these studies still need to more investigation. Here we report to investigate oblique side weir theoretically using statistical method to supported other studies in this case. Measurement uncertainty discharge coefficient C_d was obtained by two methods: analytical according to the ‘Guide to the expression of uncertainty in measurement’ and the Monte Carlo method. The results indicate that all experimental results are consistent with the analytical results. The relative expanded uncertainty of the discharge coefficient C_d does not exceed 2%.     

Analysis of flow through lateral rectangular orifices in open channels

A side lateral orifice in open channel is hydraulic control structure widely used in hydraulic, irrigation and environmental engineering for diverting the flow from main channel to a secondary channel. In this paper, analytical relationships for the discharge through side orifice are developed accounting for the pressure distribution over the area of the orifice. The computed discharges using the proposed relationship are within ±5% of the observed values; however percentage error is more in case the discharge is computed using earlier equations.     

Mill-cut: a neural network system for the prediction of thermo-mechanical loads induced in end-milling operations

This paper presents the design and implementation issues of a generalized system called mill-cut, developed for the prediction of cutting forces and temperature in end-milling operations. Based on an ANN approach, mill-cut predicts all the three components of cutting forces and average shear plane temperature for a given set of machining parameters broadly categorized into three groups viz. (i) cutting tool geometrical parameters (ii) cutting parameters and (iii) workpiece material properties. In the present work, for representing overall machining condition, 15 machining parameters having major impact on the cutting forces and cutting temperature were chosen. The feed-forward back-propagated ANN architecture has been incorporated, which was initially trained with analytical data before incorporating it as part of an integrated system. Results obtained from the proposed model show good agreement with the experimental/numerical (FEM based) results available in the literature.     

A hybrid ensemble machine learning model for discharge coefficient prediction of side orifices with different shapes

Side orifices are widely applied for flow control and regulation in channel systems. Accurate estimation of the discharge coefficient of the side orifice is significant for water management. The main objective of current research is to accurately predict the discharge coefficients of circular and rectangular side orifices. Considering that traditional empirical regressions are hard to estimate the discharge coefficient precisely due to the complex nonlinear relationship between the discharge coefficient and relevant parameters, a new hybrid boosting ensemble machine learning model, BO-XGBoost, is developed, which combines the advantages of the boosting ensemble model (XGBoost) and Bayesian Optimization. To further evaluate the proposed hybrid model, it is also compared with other tree-based machine learning models, including standalone XGBoost, Random Forest (RF) and Decision Tree (DT). Literature experimental data of the flow and geometric parameters relevant to the discharge coefficients of circular and rectangular side orifices are collected and applied to develop the models. Four dimensionless parameters of the relative channel width (B/L), the relative bottom height (W/L), the relative upstream depth (Y/L) and the upstream Froude number (Fr) are taken into consideration for the prediction of discharge coefficient (C_d). Furthermore, four different input combinations are designed and then compared to determine the best one on the basis of RMSE. By using the optimal input combination, our results demonstrate that BO-XGBoost provides the best comprehensive performance among all the involved machine learning models in the discharge coefficient prediction for both types of side orifices. Besides, the uncertainty analysis also reveals that BO-XGBoost shows the narrowest uncertainty bandwidth and gives the highest prediction reliability.     

Weighted regularized extreme learning machine to model the discharge coefficient of side slots

In the current research, a modern learning machine algorithm named “Weighted Regularized Extreme Learning Machine (WRELM)" is implemented for the first time for the simulation of the coefficient of discharge of side slots. For this purpose, an effective variable on the coefficient of discharge of side slots is firstly introduced, then five distinctive WRELM models are produced by it for the estimation of the coefficient. In the next stage, a database is created for verification of WRELM results. it should be mentioned that 70% of the data are utilized for training the WRELM models, while the rest (i.e. 30%) for testing them. After that, the optimal number of hidden layer neurons as well as the best activation function of the WRELM algorithm are chosen. In addition, the best regularization parameter and also the weight function of the WRELM are achieved. By conducting a sensitivity analysis, the most effective variable for the simulation of the coefficient of discharge along with the WRELM superior model is introduced. The WRELM superior model estimates values of the coefficient of discharge with the maximum exactness and the highest correlation. For instance, the estimations of the correlation coefficient and scatter index for this model are computed to be 0.930 and 0.051, respectively. The sensitivity analysis shows that the ratio of the side slot crest height to its length and the Froude number should be considered as the most important input variables. A comparison between the WRELM with the ELM displays that the former works much better. Furthermore, an uncertainty analysis is executed for both models. Eventually, an equation is suggested for the estimation of the coefficient of discharge and a partial derivative sensitivity analysis is performed on it.     

Research on calculation method of free flow discharge based on artificial neural network and regression analysis

Taking the Huaidian Sluice on the Shaying River in China as an example, this paper establishes the calculation model of the free flow based on artificial neural network and regression analysis. Four forms of discharge coefficient calculation equations were obtained by regression analysis, and three neural network models were established. The model is fully verified by using the measured data. The experimental results show that the third-order polynomial and multilayer perceptron neural network have better adaptability. The advantages and disadvantages of the different methods are analyzed and the cause of the error is identified. It provides a theoretical basis for dealing with the discharge calculation of small and medium dam.     

Artificial neural network-based prediction technique for wear loss quantities in Mo coatings

Mo coated materials are used in automotive, aerospace, pulp and paper industries in order to protect machine parts against wear and corrosion. In this study, the wear amounts of Mo coatings deposited on ductile iron substrates using an atmospheric plasma-spray system were investigated for different loads and environment conditions. The Mo coatings were subjected to sliding wear against AISI 303 counter bodies under dry and acid environments. In a theoretical study, cross-sectional microhardness from the surface of the coatings, loads, environment and friction test durations were chosen as variable parameters in order to determine the amount of wear loss. The numerical results obtained via a neural network model were compared with the experimental results. Agreement between the experimental and numerical results is reasonably good.     

人工神经网络在智能机械设计中的应用

介绍了智能ＣＡＤ的概念和发展,分析了人工神经网络（ＡＮＮ）的特点,针对目前机械设计专家系统存在的问题,提出将ＡＮＮ应用到专家系统的设计中,是进行智能ＣＡＤ的一条有效途径。介绍了ＡＮＮ在概念设计、设计过程中形象思维的模拟、知识的获取和表示、回溯问题的模拟等方面的应用。     

Application of hybrid neural network in discharge coefficient prediction of triangular labyrinth weir

Discharge coefficient (C_d) is an important parameter of triangular labyrinth weir. It is of great significance to predict the discharge coefficient accurately. In this research, in order to more accurately predict the C_d, in view of the traditional BP neural network is easy to fall into the local minimum in the training process, genetic algorithm (GA) and particle swarm optimization (PSO) are employed to optimize the traditional BP neural network's initial weights and thresholds. Nonlinear regression analysis (NLR) is also added to compare with these intelligent methods and four discharge coefficient prediction models are built, namely the NLR, the BPNN, the GA-BPNN and the PSO-BPNN. After the completion of the model construction, in order to objectively evaluate the performance of these models, the prediction results of these models are compared with the experiment results, and the determination coefficient (R²), the mean absolute error (MAE) and the root mean square error (RMSE) are introduced as the performance indicators to quantify the model performance. The results show that the accuracy and stability of the NLR are much worse than that of the intelligent models. The prediction results of the GA-BPNN and the PSO-BPNN are quite accurate with a higher decision coefficient than the BPNN. Moreover, the MAEs and the RMSEs of the GA-BPNN and the PSO-BPNN were significantly reduced by 25 and 40% compared with BPNN, respectively, and the maximum prediction errors were 4.4% and 2.6%, severally. Meanwhile, the width of error uncertainty band of GA-BPNN and PSO-BPNN is narrower than BPNN. By comparing GA-BPNN and PSO-BPNN with the discharge coefficient prediction models of triangular labyrinth weir in previous literatures, it is found that the mean absolute percentage error (MAPE) values of GA-BPNN and PSO-BPNN are 1.504% and 1.225% respectively, which are lower than other existing models. At the same time, the other performance indexes are better than most existing models, indicating that the genetic algorithm and PSO algorithm are more effective than the traditional BP algorithm in adjusting BP neural network parameters, easier to find the global optimal value, and improve the prediction accuracy and applicability of the model.     

A hybridized artificial neural network and imperialist competitive algorithm optimization approach for prediction of soil compaction in soil bin facility

We were inspired to furnish information concerning the promising applicability of a hybrid approach involving artificial neural networks (ANNs), with manifold network functions, and a meta-heuristic optimization algorithm for prediction of soil compaction indices. The employed network functions were the prevailed feed-forward network and the novel cascade-forward network algorithms to accommodate multivariate inputs of wheel load, tire inflation pressure, number of passage, slippage, and velocity each at three different levels for estimating the study objectives of soil compaction (i.e. penetration resistance and soil sinkage). The experimentations were carried out in a soil bin facility utilizing a single wheel-tester. Each ANN trials was developed merely and then by merging with the recently introduced evolutionary optimization technique of imperialist competitive algorithm (ICA). The results were compared on the basis of a modified performance function (MSEREG) and coefficient of determination (R²). Our results elucidated that hybrid ICA–ANN further succeeded to denote lower modeling error amongst which, cascade-forward network optimized by ICA managed to yield the highest quality solutions.     

Performance evaluation of two different neural network and particle swarm optimization methods for prediction of discharge capacity of modified triangular side weirs

Technical design of side weirs needs high accuracy in predicting discharge coefficient. In this study, discharge coefficient prediction performance of multi-layer perceptron neural network (MLPNN) and radial basis neural network (RBNN) were compared with linear and nonlinear particle swarm optimization (PSO) based equations. Performance evaluation of the model was done by using root mean squared error (RMSE), coefficient of determination (R²), mean absolute error (MAE), average absolute deviation (δ) and mean absolute relative error (MARE). Comparison of the results showed that both neural networks and PSO based equations could determine discharge coefficient of modified triangular side weirs with high accuracy. The RBNN with RMSE of 0.037 in test data was found to be better than MLPNN with RMSE of 0.044 and multiple linear and nonlinear PSO based equations (ML-PSO and MNL-PSO) with RMSE of 0.043 and 0.041, respectively. However, due to their simplicity, PSO based equations can be sufficient for use in practical cases.     

A novel neural network method for wind speed forecasting using exogenous measurements from agriculture stations

This paper proposes a novel ANN-based wind speed forecasting method based in the introduction of low-quality measurements as exogenous information, processed by six prediction models to perform one-hour-ahead enhanced forecasting. The models evaluated are classified in two groups: first, persistence and ARIMA, which are used as references, and secondly, the remaining four, based on neural networks. Model comparison is realized by applying two procedures. On the one hand, four quality indexes are assessed (the Pearson’s correlation coefficient, the index of agreement, the mean absolute error and the mean squared error), and the other hand, an ANOVA test and multiple comparison procedure are conducted. A backpropagation network with nine neurons in the hidden layer obtains improvements couples (mean absolute – mean squared error) of 23.92–47.48%, and 23.19–45.54% for the persistence and the ARIMA models, respectively. The paper provides strong practical evidence that traditional agricultural measurements are potentially capable of improving estimates and forecasts.     

人工神经网络在机械设计中的应用

本文通过对机械设计专家系统和人工神经网络的讨论，研究了人工神经网络和专家系统技术在机械设计智能系统中的综合应用问题，并提出了人工神经网络在机械设计中的总体应用方案，为进一步研究打下了基础。     

Neural network simulation metamodels and genetic algorithms in analysis and design of manufacturing cells

A manufacturing cell can be modelled using discrete event simulation in order to predict its performance under various combinations of input parameters, related to design issues as well as operation issues. Such models suffer from inherent lack of generality and are useful on a case-by-case basis. Therefore, determination of the best values of design and operation parameters in combination cannot be performed rigorously, but only on an experimentation basis. This work proposes a systematic procedure for optimisation. The final stage is an optimisation procedure using a genetic algorithm which uses the classic genetic operators tuned with due care to avoid local maxima of the fitness function. The actual value of the fitness function corresponding to each breeding case could be obtained by running the discrete event simulation application, but that would make for lengthy response. Therefore, a first stage of the optimising procedure is proposed that calculates the essential part of the fitness function by a neural network metamodel generalising on simulation results. Conditions for successful application of the above procedures are discussed.     

Application of artificial neural networks for prediction of coercivity of highly ordered cobalt nanowires synthesized by pulse electrodeposition

This study aims to predict the coercivity of cobalt nanowires fabricated by Alternating Current (AC) pulse. Coercivity is one of the most important properties of magnetic materials and its value shows the needed magnetic field in a way that magnetization of system is decreased to zero. There are many parameters such as pH of solution, oxidative and reductive times, oxidative and reductive voltages, interval between pulses (off-time), and concentration of deposition solution that have direct effect on materials magnetic properties of. Change of initial conditions to obtain the best results is very time consuming, therefore employing a method which can save both the time and cost is necessary. Hence, it this study Artificial Neural Network (ANN), which has numerous applications and has attracted many attentions in various fields, was applied. Through this study, an ANN was designed to present a template that is capable for predicting output data (coercivity) according to input data (pH, oxidative and reductive times, oxidative and reductive voltages, and off-time). Besides, in this research, the results for pH = 4 and 6 were investigated and the effect of off-time as well as the deposition time on coercivity were studied.     

人工神经网络在超声振动-放电复合加工模糊控制中的应用

针对模糊控制系统,构建了相应的人工神经网络,利用matlab6.5平台,对人工神经网络进行了训练,得出了神经网络训练后的的权重和阈值,并对系统进行了仿真。     

Optimum design of ship design system using neural network method in initial design of hull plate

Manufacturing of complex surface plates in stern and stem is a major factor in cost of a preliminary ship design by computing process. If these hull plate parts are effectively classified, it helps to compute the processing cost and find the way to cut-down the processing cost. This paper presents a new method to classify surface plates effectively in the preliminary ship design using neural network. A neural-network-based ship hull plate classification program was developed and tested for the automatic classification of ship design. The input variables are regarded as Gaussian curvature distributions on the plate. Various applicable rules of network topology are applied in the ship design. In automation of hull plate classification, two different numbers of input variables are used. By observing the results of the proposed method, the effectiveness of the proposed method is discussed. As a result, high prediction rate was achieved in the ship design. Accordingly, to the initial design stage, the ship hull plate classification program can be used to predict the ship production cost. And the proposed method will contribute to reduce the production cost of ship.     

神经网络集成方法在产品完工期预测中的应用

针对单个神经网络模型易出现过拟合而导致泛化能力较弱的缺点,引入了神经网络集成方法,对传统的Bagging方法进行改进,提出了一种基于0.632误差聚类的Bagging方法.通过实验对比和假设检验,证实了该方法的优越性,并探讨了最佳聚类数目.最后,通过应用实例展示了利用集成神经网络进行产品完工期预测的全过程.实验结果显示,该方法明显地提高了预测精度.