人工神经网络在成矿预测中的应用

成矿预测正从定性描述性预测向定量成矿预测转变,数理统计方法和技术逐渐引入地学研究。传统统计方法多假想包含地学现象的空间为均质,假定在一个尺度上的地学关系在另一个尺度上也是相同的,而在实际应用中这样的地质条件是不可能存在的。而非线性科学正具有不满足线性叠加原理的性质,因此将非线性科学如人工神经网络与成矿预测相结合是未来矿产资源预测的发展方向。采用Kohonen聚类模型和BP预测模型相结合的方法,对包古图金矿区1 444个矿点的地球化学数据进行聚类分析并建立成矿预测模型,预测正确率为85.2%。该方法性能良好,具有一定的实际意义,为解决成矿预测提供了一种新的手段。     

Surface roughness prediction in turning using artificial neural network

In this work, a back propagation neural network model has been developed for the prediction of surface roughness in turning operation. A large number of experiments were performed on mild steel work-pieces using high speed steel as the cutting tool. Process parametric conditions including speed, feed, depth of cut, and the measured parameters such as feed and the cutting forces are used as inputs to the neural network model. Roughness of the machined surface corresponding to these conditions is the output of the neural network. The convergence of the mean square error both in training and testing came out very well. The performance of the trained neural network has been tested with experimental data, and found to be in good agreement.     

The prediction of meteorological variables using artificial neural network

Artificial neural network model had been implemented in different areas such as industrial processes, sciences, and business. In these days, climatic changes have occurred. In this study, meteorological variables are predicted using ANN model. The experimental values are obtained from the Turkish Meteorological Center for different measurement stations. The prediction of the meteorological values are realized, when the neural network model have been trained and tested. Obtained results show that the difference between estimated and measured values is very low. The neural network models for prediction are successfully applied to the meteorological variables.     

Backbreak prediction in the Chadormalu iron mine using artificial neural network

Backbreak is one of the unfavorable blasting results, which can be defined as the unwanted rock breakage behind the last row of blast holes. Blast pattern parameters, like stemming, burden, delay timing, stiffness ratio (bench height/burden) and rock mass conditions (e.g., geo-mechanical properties and joints), are effective in backbreak intensity. Till date, with the exception of some qualitative guidelines, no specific method has been developed for predicting the phenomenon. In this paper, an effort has been made to apply artificial neural networks (ANNs) for predicting backbreak in the blasting operation of the Chadormalu iron mine (Iran). Number of ANN models with different hidden layers and neurons were tried, and it was found that a network with architecture 10-7-7-1 is the optimum model. A comparative study also approved the superiority of the ANN modeling over the conventional regression analysis. Mean square error (MSE), variance account for (VAF) and coefficient of determination (R ²) between measured and predicted backbreak for the ANN model were calculated and found 89.46 %, 0.714 and 90.02 %, respectively. Also, for the regression model, MSE, VAF and R ² were computed and found 66.93 %, 1.46 and 68.10 %, respectively. Sensitivity analysis was also carried out to find out the influence of each input parameter on backbreak results, and it was revealed that burden is the most influencing parameter on the backbreak, whereas water content is the least effective parameter in this regard.     

改进的高阶神经网络在汇率市场预测中的应用

评价了神经网络和高阶神经网络的性能,并提出了一种新型的具有运算效率高和算法精确等特点的随机高阶神经网络.模拟结果展示了这种模型的可行性和有效性.     

人工神经网络在ERP系统中的应用

在传统的ERP的基础上,增加专家系统模块,即基于人工神经网络技术的预测分析模块,提出了ERP和专家系统的集成管理方法,完成复杂的非线性预测,以使ERP系统智能化、自动化水平更高。该模块采用反向传输BP神经网络模型来实现,通过网络的自适应学习和训练,找出输入和输出之间的内在联系,以求解问题。利用该专家系统对汽车制造企业市场销售量进行预测,结果表明：该方法性能、实用性和通用性好。     

MOE prediction in Abies pinsapo Boiss. timber: Application of an artificial neural network using non-destructive testing

Determining the modulus of elasticity of wood by applying an artificial neural network using the physical properties and non-destructive testing can be a useful method in assessments of the timber structure in old constructions. The modulus of elasticity of Abies pinsapo Boiss. timber was predicted in this study through the parameters of density, width, thickness, moisture content, ultrasonic wave propagation velocity and visual grading of the test pieces. A feedforward multilayer perceptron network was designed for this purpose, achieving 75.0% success in the testing or unknown group.     

The prediction of SO2 removal using statistical methods and artificial neural network

Artificial neural network and a statistical model have been applied in a laboratory scale trickle bed reactor (TBR) to investigate the SO₂ removal efficiency of activated carbon. The performance of artificial neural network (ANN) model has been compared with the statistical model based on central composite experimental design. Two independent variables, which affect the amount of SO₂ removal by the liquid phase in the TBR, were selected; namely liquid flow rate and gas flow rate. Amount of SO₂ removal was chosen as the dependent variable (target data). A second order statistical model has been considered to show the dependence of the amount of SO₂ removal on the operating parameters. A back-propagation ANN has been used to develop a model relating to the amount of SO₂ removal. A series of experiments have been conducted on the basis of the statistics-based design of experimental method. It is observed that a neural network architecture having one input layer with two neurons, one hidden layer with three neurons, one output layer with one neuron and an epoch size of 20 gives better prediction. The predictions are more accurate than those obtained from regression models.     

基于粒子群优化算法的神经网络在油品质量预测中的应用

粒子群优化算法是基于群体智能的全局优化技术,它通过了粒子间的相互作用,对解空间进行智能搜索,从而发现最优解。其优势在于操作简单,容易实现。文中将粒子群算法和神经网络进行融合,优化神经网络的权值和域值,充分发挥了粒子群算法的全局寻优能力和BP算法的局部搜索优势,并与改进的BP算法进行了比较 。油品质量预测的实例表明,将粒子群算法用于神经网络的优化,收敛速度更快,预测精度更高,而且算法简单。     

Performance comparison of artificial neural network models for daily rainfall prediction

With an aim to predict rainfall one-day in advance, this paper adopted different neural network models such as feed forward back propagation neural network (BPN), cascade-forward back propagation neural network (CBPN), distributed time delay neural network (DTDNN) and nonlinear autoregressive exogenous network (NARX), and compared their forecasting capabilities. The study deals with two data sets, one containing daily rainfall, temperature and humidity data of Nilgiris and the other containing only daily rainfall data from 14 rain gauge stations located in and around Coonoor (a taluk of Nilgiris). Based on the performance analysis, NARX network outperformed all the other networks. Though there is no major difference in the performances of BPN, CBPN and DTDNN, yet BPN performed considerably well confirming its prediction capabilities. Levenberg Marquardt proved to be the most effective weight updating technique when compared to different gradient descent approaches. Sensitivity analysis was instrumental in identifying the key predictors.     

Application of artificial neural networks for water quality prediction

The term “water quality” is used to describe the condition of water, including its chemical, physical, and biological characteristics. Modeling water quality parameters is a very important aspect in the analysis of any aquatic systems. Prediction of surface water quality is required for proper management of the river basin so that adequate measure can be taken to keep pollution within permissible limits. Accurate prediction of future phenomena is the life blood of optimal water resources management. The artificial neural network is a new technique with a flexible mathematical structure that is capable of identifying complex non-linear relationships between input and output data when compared to other classical modeling techniques. Johor River Basin located in Johor state, Malaysia, which is significantly degrading due to human activities and development along the river. Accordingly, it is very important to implement and adopt a water quality prediction model that can provide a powerful tool to implement better water resource management. Several modeling methods have been applied in this research including: linear regression models (LRM), multilayer perceptron neural networks and radial basis function neural networks (RBF-NN). The results showed that the use of neural networks and more specifically RBF-NN models can describe the behavior of water quality parameters more accurately than linear regression models. In addition, we observed that the RBF finds a solution faster than the MLP and is the most accurate and most reliable tool in terms of processing large amounts of non-linear, non-parametric data.

     

Enhancement of artificial neural network learning using centripetal accelerated particle swarm optimization for medical diseases diagnosis

In recent decades, artificial neural networks (ANNs) have been extensively applied in different areas such as engineering, medicine, business, education, manufacturing and so on. Nowadays, ANNs are as a hot research in medicine especially in the fields of medical disease diagnosis. To have a high efficiency in ANN, selection of an appropriate architecture and learning algorithm is very important. ANN learning is a complex task and an efficient learning algorithm has a significant role to enhance ANN performance. In this paper, a new meta-heuristic algorithm, centripetal accelerated particle swarm optimization (CAPSO), is applied to evolve the ANN learning and accuracy. The algorithm is based on an improved scheme of particle swarm algorithm and Newton’s laws of motion. The hybrid learning of CAPSO and multi-layer perceptron (MLP) network, CAPSO-MLP, is used to classify the data of nine standard medical datasets of Hepatitis, Heart Disease, Pima Indian Diabetes, Wisconsin Prognostic Breast Cancer, Parkinson’s disease, Echocardiogram, Liver Disorders, Laryngeal 1 and Acute Inflammations. The performance of CAPSO-MLP is compared with those of PSO, gravitational search algorithm and imperialist competitive algorithm on MLP. The efficiency of methods are evaluated based on mean square error, accuracy, sensitivity, specificity, area under the receiver operating characteristics curve and statistical tests of \(t\) -test and Wilcoxon’s signed ranks test. The results indicate that CAPSO-MLP provides more effective performance than the others for medical disease diagnosis especially in term of unseen data (testing data) and datasets with high missing data values.     

模糊粒子群神经网络算法及应用

针对流程工业神经网络建模时,BP算法的局部收敛问题,采用模糊粒子群算法改进神经网络学习问题。该算法将模糊粒子群引入神经网络学习算法,使得粒子群的权重自适应更新,同时模糊粒子群自适应调整神经网络权重参数,改进网络收敛性。将算法用于建立乙烯裂解炉出口温度(COT)、裂解产品收率软测量模型,取得了较好的应用效果。     

Online tool wear prediction in drilling operations using selective artificial neural network ensemble model

Online tool wear prediction plays a key role in industry automation for higher productivity and product quality. In recent past, several artificial neural network (ANN) models using multiple sensor signals as inputs for prediction as well as classification of tool wear have been proposed. However, a single ANN used in these models is often tries, which could limit their wide applications due to the complicated procedure of constructing a single ANN model. This study proposed a selective ANN ensemble approach DPSOEN, where several selected component ANNs are jointly used to online predict flank wear in drilling operation. DPSOEN provides more simple training and better generalization performance than using single ANN and hence is easier to be used by operators who often are not good at ANN techniques. Two benchmark cases were used to evaluate the performance of DPSOEN in predicting flank wear. It shows improved generalization performance that outperforms those of single ANN and Ensemble ALL approach. The investigation proposed a heuristic approach for applying the DPSOEN-based model as an effective and useful tool to predict tool wear online with potential applications for tool condition monitoring in general. Analysis from this study provides guidelines in developing ANN ensemble-based tool wear prediction systems.     

Approximating support vector machine with artificial neural network for fast prediction

Support vector machine (SVM) is a powerful algorithm for classification and regression problems and is widely applied to real-world applications. However, its high computational load in the test phase makes it difficult to use in practice. In this paper, we propose hybrid neural network (HNN), a method to accelerate an SVM in the test phase by approximating the SVM. The proposed method approximates the SVM using an artificial neural network (ANN). The resulting regression function of the ANN replaces the decision function or the regression function of the SVM. Since the prediction of the ANN requires significantly less computation than that of the SVM, the proposed method yields faster test speed. The proposed method is evaluated by experiments on real-world benchmark datasets. Experimental results show that the proposed method successfully accelerates SVM in the test phase with little or no prediction loss.     

Soot emission prediction of a waste-gated turbo-charged DI diesel engine using artificial neural network

This study is about soot emission prediction of a waste-gated turbo-charged DI diesel engine using artificial neural network (ANN). For training the ANN model, six ranges of experimental data in previous study were used, and one range of data was kept for testing the accuracy of ANN predictions. The input parameters for the ANN are inlet manifold pressure, inlet manifold temperature, inlet air mass flow rate, fuel consumption, engine torque, and speed. Output parameter is the density of soot in the exhaust. The results show the ANN approach can be used to accurately predict soot emission of a turbo-charged diesel engine in different opening ranges of waste-gate (ORWG). Root mean-squared error (RMSE), fraction of variance (R ²), and mean absolute percentage error (MAPE) for predictions were found to be 1.19 (mg/m³), 0.9998, and 6.4%, respectively.     

Fault size diagnosis of rolling element bearing using artificial neural network and dimension theory

Neural Computing and Applications - Failure of roller bearings can cause downtime or a complete shutdown of rotating machines. Therefore, a well-timed detection of bearing defects must be...     

Estimation of all-terminal network reliability using an artificial neural network

The exact calculation of all-terminal network reliability is an NP-hard problem, with computational effort growing exponentially with the number of nodes and links in the network. During optimal network design, a huge number of candidate topologies are typically examined with each requiring a network reliability calculation. Because of the impracticality of calculating all-terminal network reliability for networks of moderate to large size, Monte Carlo simulation methods to estimate network reliability and upper and lower bounds to bound reliability have been used as alternatives. This paper puts forth another alternative to the estimation of all-terminal network reliability — that of artificial neural network (ANN) predictive models. Neural networks are constructed, trained and validated using the network topologies, the link reliabilities, and a network reliability upperbound as inputs and the exact network reliability as the target. A hierarchical approach is used: a general neural network screens all network topologies for reliability followed by a specialized neural network for highly reliable network designs. Both networks with identical link reliability and networks with varying link reliability are studied. Results, using a grouped cross-validation approach, show that the ANN approach yields more precise estimates than the upperbound, especially in the worst cases. Using the reliability estimation methods of the ANN, the upperbound and backtracking, optimal network design by simulated annealing is considered. Results show that the ANN regularly produces superior network designs at a reasonable computational cost.Scope and purposeAn important application area of operations research is the design of structures, products or systems where both technical and business aspects must be considered. One expanding design domain is the design of computer or communications networks. While cost is a prime consideration, reliability is equally important. A common reliability measure is all-terminal reliability, the probability that all nodes (computers or terminals) on the network can communicate with all others. Exact calculation of all-terminal reliability is an NP-hard problem, precluding its use during optimal network topology design, where this calculation must be made thousands or millions of times. This paper presents a novel computationally practical method for estimating all-terminal network reliability. Is shown how a neural network can be used to estimate all-terminal network reliability by using the network topology, the link reliabilities and an upperbound on all-terminal network reliability as inputs. The neural network is trained and validated on a very minute fraction of possible network topologies, and once trained, it can be used without restriction during network design for a topology of a fixed number of nodes. The trained neural network is extremely fast computationally and can accommodate a variety of network design problems. The neural network approach, an upper bound approach and an exact backtracking calculation are compared for network design using simulated annealing for optimization and show that the neural network approach yields superior designs at manageable computational cost.     

Emergence of adaptive behaviors for artificial creature using a combined artificial neural network

This study proposes a method to acquire adaptive behavior for artificial creature which has a lot of joints using a combined Artificial Neural Network (ANN). Experiment in this study focuses on artificial fish model, which has a lot of joints, tracking towards a target in the virtual water environment. In order to control motions of joints, a combined ANN is implemented with the model. At first, one ANN is prepared to control specific joints so as to swim basically in response to minimal input information using evolutionary computation in preliminary experiments. And an new network is constructed by combining its network and the other network. In order to acquire complicate behavior for artificial creature, weights of combined ANN are optimized. Experiment result shows the model which has many joints acquire adaptive swimming behavior towards a target by optimizing combined network.     

Spectral prediction of Phytophthora infestans infection on tomatoes using artificial neural network (ANN)

Late blight (LB) is one of the most aggressive tomato diseases in California. Accurately detecting the disease will increase the efficiency of properly controlling the disease infestations to ensure the crop production. In this study, we developed a method to spectrally predict late blight infections on tomatoes based on artificial neural network (ANN). The ANN was designed as a back‐propagation (BP) neural network that used gradient‐descent learning algorithm. Through comparing different network structures, we selected a 3‐25‐9‐1 network structure. Two experimental samples, from field experiments and remotely sensed image data sets, were used to train the ANN to predict healthy and diseased tomato canopies with various infection stages for any given spectral wavelength (µm) intervals. Results of discrete data indicated different levels of disease infestations. The correlation coefficients of prediction values and observed data were 0.99 and 0.82 for field data and remote sensing image data, respectively. In addition, we predicted the field data based on the remote sensing image data and predicted the remote sensing image data with field data using the same network structure, and the results showed that the coefficient of determination was 0.62 and 0.66, respectively. Our study suggested an ANN with back‐propagation training could be used in spectral prediction in the study.