主成分分析法用于化工过程人工神经网络建模

达到提出应用主成分分析法对样本进行预处理,减少网络的输入因子数,消除输入因子间的关相性关简化网络结构,达到提高网络学习速率的目的,得到的人工神经网络模型能达到所要求的精度。     

Customer churn prediction by hybrid neural networks

As churn management is a major task for companies to retain valuable customers, the ability to predict customer churn is necessary. In literature, neural networks have shown their applicability to churn prediction. On the other hand, hybrid data mining techniques by combining two or more techniques have been proved to provide better performances than many single techniques over a number of different domain problems. This paper considers two hybrid models by combining two different neural network techniques for churn prediction, which are back-propagation artificial neural networks (ANN) and self-organizing maps (SOM). The hybrid models are ANN combined with ANN (ANN + ANN) and SOM combined with ANN (SOM + ANN). In particular, the first technique of the two hybrid models performs the data reduction task by filtering out unrepresentative training data. Then, the outputs as representative data are used to create the prediction model based on the second technique. To evaluate the performance of these models, three different kinds of testing sets are considered. They are the general testing set and two fuzzy testing sets based on the filtered out data by the first technique of the two hybrid models, i.e. ANN and SOM, respectively. The experimental results show that the two hybrid models outperform the single neural network baseline model in terms of prediction accuracy and Types I and II errors over the three kinds of testing sets. In addition, the ANN + ANN hybrid model significantly performs better than the SOM + ANN hybrid model and the ANN baseline model.     

Analysis of a variable speed vapor compression system using artificial neural networks

An artificial neural network (ANN) is a mathematical model that is inspired by the operation of biological neural networks. However, this is typically considered a computational model. An ANN can easily adapt to multiple situations and extract information that is apparently hidden in a system.An ANN can be used in three basic configurations: mapping, auto-association and classification. An ANN in a mapping configuration can be used to model a two port system with inputs and outputs. Therefore, a vapor compression system can be modeled using an ANN in a mapping configuration. That is, some parameters from the compression system can be used for input while other parameters can be used as output. The simulation experiments include the design, training and validation of a set of ANNs to find the best architecture while minimizing over-fitting.This paper presents a new method to model a variable speed vapor compression system. This method accurately estimates the number of neurons in the hidden layer of an ANN. The analysis and the experimental results provide new insights to understand the dependency between the input and output parameters in a vapor compression system, concluding that the model can predict the energetic performance of a variable speed vapor compression system. Additionally, the simulation results indicate that an ANN can extract, from the data sets, information that is implicit in the configuration of the vapor compression system.     

Nonlinear system identification of large-scale smart pavement systems

This paper proposes a novel model for predicting complex behavior of smart pavements under a variety of environmental conditions. The mathematical model is developed through an adaptive neuro fuzzy inference system (ANFIS). To evaluate the effectiveness of the ANFIS model, the temperature fluctuations at different locations in smart pavement systems equipped with pipe network systems under solar radiations is investigated. To develop the smart pavement ANFIS model, various sets of input and output field experimental data are collected from large-scale experimental test beds. The solar radiation and the inlet water flow are used as input signals for training complex behavior of the smart pavement ANFIS model, while the temperature fluctuation of the smart pavement system is used for the output signal. The trained model is validated using 20 different data sets that are not used for the training process. It is demonstrated from the simulation that the ANFIS identification approach is effective in modeling complex behavior of the pavement–fluid system under a variety of environmental conditions. Comparison with high fidelity data proves the viability of the proposed approach in pavement health monitoring setting, as well as automatic control systems.     

Constructing of the risk classification model of cervical cancer by artificial neural network

It is significant to build up the risk classification model of cervical cancer for the evaluation of high-risk population. Data were divided into two sub-data, one is model building sub-data, the other is model testing sub-data. By using of artificial neural network (ANN) analysis method (Back Propagation, BP), the risk classification model had been setup. The parameters were listed as following: the data had been treated as normalization, and the level of network was 3, and the number of neural in hidden level was 5, and the transmitting function between input level and hidden level was logsig, and the transmitting function between hidden level and output level was purelin, and the studying method was Levenberg–Marquardt optimizing, and the error parameter eg = 0.09, maximum epochs me = 8000. The model quality was good (sensitivity = 98%, specificity = 97%), and the back calculation fitting result was excellent. The predictive value of 10 unknown data was also good, during which the correct rate of control group was 100%, and that of case group was 80%. Because ANN is with the character of self-organizing, self-learning and self-adapting, the ANN risk classification model is fit for the screening of high-risk population of local cervical cancer, risk evaluation of cervical cancer and the effect evaluation of the prevention method after training the model by new data of some area.     

An Integrated Model for Customer Relationship Management: An Analysis and Empirical Study

Ensuring customer satisfaction and maintaining long‐term relationships with customers have become essential for survival among competitive service industries. The present study addresses this need by proposing a conceptually integrated four‐phase model that incorporates elements of customer relationship management (CRM) and customer satisfaction (represented by the extended American Customer Satisfaction Index model). Then, this study formulates structural equation modeling to test various research hypotheses related to the effect of the CRM initiative. An empirical study of 143 leading Taiwanese service firms distributed among seven service industries was conducted. The implementation levels of various constructs, input (customer knowledge), service provision (customer interactions), output quality, perceived quality, perceived value, perception of customer satisfaction, customer loyalty, purchasing intention, and profits of CRM are assessed in a range of service industries by means of a questionnaire survey and in‐depth interviews. The results of the empirical study reveal statistically significant influences among various constructs of the CRM integrated model. These results also represent a useful reference for managers of service organizations that could be used to improve the profitability and implementation level of CRM. The present study represents an important investigation in the development of an integrated CRM implementation system for service industries. © 2012 Wiley Periodicals, Inc.     

Artificial neural networks for predicting indoor temperature using roof passive cooling techniques in buildings in different climatic conditions

Three passive cooling methods (e.g. roof pond, reflective roof cooling and using insulation over the roof) have been experimentally evaluated using an experimental test structure. The objective of this work is to train an artificial neural network (ANN) to learn and predict the indoor temperature of room with the different experimental data. Different training algorithms (traingd, traingdm, traingdx, trainrp, traincgp, traincgf, traincgb, trainscg, trainbfg, trainoss, trainlm, and trainbr) were used to create an ANN model. This study is helpful in finding the thermal comfort of building by applying different passive cooling techniques. The data presented as input were outside temperature, relative humidity, solar intensity and wind speed. The network output was indoor temperature. The advantages of this approach are (i) the speed of calculation, (ii) the simplicity, (iii) adaptive learning from examples and thus gradually improve its performance, (iv) self-organization and (vi) real time operation. Results proved highly satisfactory and provided enough confidence for the process to be extended to a larger solution space for which there is uneconomical and time consuming way of calculating the solution.     

面向图像识别的多层脉冲神经网络学习算法综述

相较于第1代和第2代神经网络,第3代神经网络的脉冲神经网络是一种更加接近于生物神经网络的模型,因此更具有生物可解释性和低功耗性。基于脉冲神经元模型,脉冲神经网络可以通过脉冲信号的形式模拟生物信号在神经网络中的传播,通过脉冲神经元的膜电位变化来发放脉冲序列,脉冲序列通过时空联合表达不仅传递了空间信息还传递了时间信息。当前面向模式识别任务的脉冲神经网络模型性能还不及深度学习,其中一个重要原因在于脉冲神经网络的学习方法不成熟,深度学习中神经网络的人工神经元是基于实数形式的输出,这使得其可以使用全局性的反向传播算法对深度神经网络的参数进行训练,脉冲序列是二值性的离散输出,这直接导致对脉冲神经网络的训练存在一定困难,如何对脉冲神经网络进行高效训练是一个具有挑战的研究问题。本文首先总结了脉冲神经网络研究领域中的相关学习算法,然后对其中主要的方法:直接监督学习、无监督学习的算法以及ANN2SNN的转换算法进行分析介绍,并对其中代表性的工作进行对比分析,最后基于对当前主流方法的总结,对未来更高效、更仿生的脉冲神经网络参数学习方法进行展望。     

An artificial neural network method for remaining useful life prediction of equipment subject to condition monitoring

Accurate equipment remaining useful life prediction is critical to effective condition based maintenance for improving reliability and reducing overall maintenance cost. In this paper, an artificial neural network (ANN) based method is developed for achieving more accurate remaining useful life prediction of equipment subject to condition monitoring. The ANN model takes the age and multiple condition monitoring measurement values at the present and previous inspection points as the inputs, and the life percentage as the output. A function generalized from the Weibull failure rate function is used to fit each condition monitoring measurement series for a failure history, and the fitted measurement values are used to form the ANN training set so as to reduce the effects of the noise factors that are irrelevant to the equipment degradation. A validation mechanism is introduced in the ANN training process to improve the prediction performance of the ANN model. The proposed ANN method is validated using real-world vibration monitoring data collected from pump bearings in the field. A comparative study is performed between the proposed ANN method and an adapted version of a reported method, and the results demonstrate the advantage of the proposed method in achieving more accurate remaining useful life prediction.     

基于人工神经网络在模具数控加工中的应用

形状特殊的模具在加工之前,应先根据一些已知条件确定出合理的加工参数,而获得最佳加工参数的途径之一就是用最优化技术对问题求解。由于模具数控加工中加工参数与加工质量之间存在高度的非线性关系,而人工神经网络（Artificial Neural Networks简称ANN）又十分擅长表达输入输出关系不明确的高度非线性关系,故基于ANN的优化设计,是解决模具数控加工技术参数优化的有效途径。     

Prediction of the effect of varying cure conditions and w/c ratio on the compressive strength of concrete using artificial neural networks

The present study aims at developing an artificial neural network (ANN) to predict the compressive strength of concrete. A data set containing a total of 72 concrete samples was used in the study. The following constituted the concrete mixture parameters: two distinct w/c ratios (0.63 and 0.70), three different types of cements and three different cure conditions. Measurement of compressive strengths was performed at 3, 7, 28 and 90 days. Two different ANN models were developed, one with 4 input and 1 output layers, 9 neurons and 1 hidden layer, and the other with 5, 6 neurons, 2 hidden layers. For the training of the developed models, 60 experimental data sets obtained prior to the process were used. The 12 experimental data not used in the training stage were utilized to test ANN models. The researchers have reached the conclusion that ANN provides a good alternative to the existing compressive strength prediction methods, where different cements, ages and cure conditions were used as input parameters.     

基于WEKA的客户分类信息系统研究

本文采用决策树方法,对客户交易数据和客户基本信息进行数据挖掘分析,降低了数据冗余度,提高了数据集准确率。在RFM模型基础上,从客户交易信息中选取了购买频率和平均每次购买金额作为分类评估指标的补充,得到一组客户交易数据训练集。结合J48算法使用WEKA算法对客户交易数据训练集进行训练、测试和验证,构建了客户分类决策模型,从而有利于客户分类原型系统的系统分析和系统设计。     

Calibrating artificial neural networks by global optimization

Artificial neural networks (ANNs) are used extensively to model unknown or unspecified functional relationships between the input and output of a “black box” system. In order to apply the generic ANN concept to actual system model fitting problems, a key requirement is the training of the chosen (postulated) ANN structure. Such training serves to select the ANN parameters in order to minimize the discrepancy between modeled system output and the training set of observations. We consider the parameterization of ANNs as a potentially multi-modal optimization problem, and then introduce a corresponding global optimization (GO) framework. The practical viability of the GO based ANN training approach is illustrated by finding close numerical approximations of one-dimensional, yet visibly challenging functions. For this purpose, we have implemented a flexible ANN framework and an easily expandable set of test functions in the technical computing system Mathematica. The MathOptimizer Professional global-local optimization software has been used to solve the induced (multi-dimensional) ANN calibration problems.     

Evolutionary learning based sustainable strain sensing model for structural health monitoring of high-rise buildings

Strain sensor network-based structural health monitoring systems have been used to assess the safety of high-rise buildings. In consideration of life cycle of high-rise buildings, long-term measurement by sensors should be required. However, because of unpredictable problems such as the lack of durability of sensors and data loggers, disruption in communication, and loss of data, long-term strain measurement of major structural members is currently infeasible. For sustainable safety assessment of high-rise buildings, this paper presents a sustainable strain-sensing model that employs an artificial neural network (ANN) to estimate the strain responses of columns depending on the wind-induced behavior of high-rise buildings. The ANN model used in the paper is based on evolutionary learning consists of training in radial basis function neural network (RBFN) and evolving in genetic algorithm. In this evolutionary RBFN (ERBFN). Weights between layers are trained and variables of Gaussian function in the RBFN are evolved to estimate strain responses of the column of the high-rise building structure. A wind tunnel test was performed to produce wind data and strains in column members in a high-rise building model. In the wind tunnel test, a specimen consisting of a core, perimeter columns, and outriggers is used to simulate the conditions of typical high-rise buildings with a slenderness ratio of 5.0. The proposed model is trained and verified by using the wind data such as wind speeds and directions and the corresponding strains measured with fiber optic grating sensors. In addition to estimation of the maximum and minimum values of strains in vertical members in a high-rise building, it is found that the proposed model can build a relationship between the wind data and strain of vertical members.     

Regularized versus non-regularized neural network model for prediction of saturated soil-water content on weathered granite soil formation

Modeling unsaturated water flow in soil requires knowledge of the hydraulic properties of soil. However, correlation between soil hydraulic properties such as the relationship between saturated soil-water content θ _s and saturated soil hydraulic conductivity k _s as function of soil depth is in stochastic pattern. On the other hand, soil-water profile process is also believed to be highly non-linear, time varying, spatially distributed, and not easily described by simple models. In this study, the potential of implementing artificial neural network (ANN) model was proposed and investigated to map the soil-water profile in terms of k _s and θ _s with respect to the soil depth d. A regularized neural network (NN) model is developed to overcome the drawbacks of conventional prediction techniques. The use of regularized NN advantaged avoid over-fitting of training data, which was observed as a limitation of classical ANN models. Site experimental data sets on the hydraulic properties of weathered granite soils were collected. These data sets include the observed values of saturated and unsaturated hydraulic conductivities, saturated water contents, and retention curves. The proposed ANN model was examined utilizing 49 records of data collected from field experiments. The results showed that the regularized ANN model has the ability to detect and extract the stochastic behavior of saturated soil-water content with relatively high accuracy.     

Prediction of wind-induced pressures on a large gymnasium roof using artificial neural networks

The application of artificial neural networks (ANNs) to solve wind engineering problems has received increasing interests in recent years. This paper is concerned with developing two ANN approaches (a backpropagation neural network [BPNN] and a fuzzy neural network [FNN]) for the prediction of mean, root-mean-square (rms) pressure coefficients and time series of wind-induced pressures on a large gymnasium roof. In this study, simultaneous pressure measurements are made on a large gymnasium roof model in a boundary layer wind tunnel and parts of the model test data are used as the training sets for developing two ANN models to recognize the input–output patterns. Comparisons of the prediction results by the two ANN approaches and those from the wind tunnel test are made to examine the performance of the two ANN models, which demonstrates that the two ANN approaches can successfully predict the pressures on the entire surfaces of the large roof on the basis of wind tunnel pressure measurements from a certain number of pressure taps. Moreover, the FNN approach is found to be superior to the BPNN approach. It is shown through this study that the developed ANN approaches can be served as an effective tool for the design and analysis of wind effects on large roof structures.     

An expert system based on artificial neural network for predicting the tensile behavior of tailor welded blanks

The forming behavior of tailor welded blanks (TWB) is influenced by thickness ratio, strength ratio, and weld conditions in a synergistic fashion. In most of the cases, these parameters deteriorate the forming behavior of TWB. It is necessary to predict suitable TWB conditions for achieving better-stamped product made of welded blanks. This is quite difficult and resource intensive, requiring lot of simulations or experiments to be performed under varied base material and weld conditions. Automotive sheet part designers will be greatly benefited if an ‘expert system’ is available that can deliver forming behavior of TWB for varied weld and blank conditions. This work primarily aims at developing an artificial neural network (ANN) model to predict the tensile behavior of welded blanks made of steel grade and aluminium alloy base materials. The important tensile characteristics of TWB are predicted within chosen range of varied blank and weld condition. Through out the work, PAM STAMP 2G^® finite element (FE) code is used to simulate the tensile behavior and to generate output data required for training the ANN. Predicted results from ANN model are compared and validated with FE simulation for two different intermediate TWB conditions. It is observed that the results obtained from ANN are encouraging with acceptable prediction errors. An expert system framework is proposed using the trained ANN for designing TWB conditions that will deliver better formed TWB products.     

酸轧机组人工神经网络仿真系统

为了提高酸轧机组的控制模型精度,消除模型训练的出错故障,为系统增加了人工干预和在线调整功能。在对原系统进行了深入分析的基础之上,建立了新的酸轧机组人工神经网络仿真系统,并通过网络与生产系统直接相连。制定了数据筛选方法．设计了调整过程算法和离线人工神经网络算法。通过实际应用所产生的数据进行分析．经过离线训练后的全局误差要比初始误差要小得多,误差精度从10^-2提高到了10^-4。实际应用结果表明了系统的有效性和实用性。     

Ability to forecast unsteady aerodynamic forces of flapping airfoils by artificial neural network

The ability of artificial neural networks (ANN) to model the unsteady aerodynamic force coefficients of flapping motion kinematics has been studied. A neural networks model was developed based on multi-layer perception (MLP) networks and the Levenberg–Marquardt optimization algorithm. The flapping kinematics data were divided into two groups for the training and the prediction test of the ANN model. The training phase led to a very satisfactory calibration of the ANN model. The attempt to predict aerodynamic forces both the lift coefficient and drag coefficient showed that the ANN model is able to simulate the unsteady flapping motion kinematics and its corresponding aerodynamic forces. The shape of the simulated force coefficients was found to be similar to that of the numerical results. These encouraging results make it possible to consider interesting and new prospects for the modelling of flapping motion systems, which are highly non-linear systems.     

A two-phased SEM-neural network approach for consumer preference analysis

A fundamental task in the design of consumer products is consumer preference analysis. The primary focus of this task is establishing a mapping relationship between product parameters/attributes and consumer preferences. The key to connect the consumer space and the design space are user perceptions of the product. Among the many existing methods, the Structural Equation Model (SEM) is one of the most used methods because it explains the causal relationship between the input and the output variables explicitly. However, the relationship obtained from the conventional SEM is linear, which is usually not the case in practice. Fortunately, the Artificial Neural Network (ANN) provides a new perspective for building nonlinear models because of its nonlinear nature. Therefore, a two-phased SEM-NN approach for consumer preference analysis is introduced for identifying and mapping how product attributes affecting the fulfillment of user perceptions and ultimately their preferences. In this model, the consumer preference analysis is conducted in two phases: influence path construction, and path coefficient revision. The proposed method can reserve the original SEM topology that reflects the causal relationship between variables while using the training algorithm of ANN to obtain more accurate path coefficients. This model could help the designers to identify and map how product attributes affecting the consumer preferences, and to better understand the factors that affect user perceptions and the inner relationships between them. To demonstrate effectiveness of the model, a case study of smartphone is presented. It is shown that the SEM-NN model can make full use of the causal analysis of SEM and the nonlinear nature of ANN and ultimately provides more reliable results of consumer preference analysis.