An interactive multi-objective artificial neural network approach for machine setup optimization

In this paper, we develop an artificial neural network method for machine setup problems. We show that our new approach solves a very challenging problem in the area of machining i.e. machine setup. A review of machine setup concepts and methods, along with feedforward artificial neural network is presented. We define the problem of machine setup to assessing the values of machine speed, feed and depth of cut (process inputs) for a particular objective such as minimize cost, maximize productivity or maximize surface finish. We use cutting temperature, cutting force, tool life, and surface roughness (process outputs) rather than objective functions to communicate with the decision maker. We show the relationship between process inputs to process outputs. This relationship is used in determining machine setup parameters (speed, feed, and depth of cut). Back propagation neural network is used as a decision support tool. The network maps, the forward relationship, and backward relationship between process inputs and process outputs. This mapping facilitates an interactive session with the decision maker. The process input is appropriately selected. Our method has the advantage of forecasting machine setup parameters with very little resource requirement in terms of time, machine tool, and people. Forecast time is almost instantaneous. Accuracy of the forecast depends on training and a well determined training sample provides very high accuracy. Trained network replaces the knowledge of an experienced worker, hence labor cost can be potentially reduced.     

计算机网络安全综合评价的神经网络模型

灰色评价法、模糊综合评价等需确定隶属函数、各指标权重,明显受人为因素的影响。尝试应用神经网络技术进行网络安全的综合评价,并通过在单指标评价标准范围内随机取值方法,生成建立神经网络模型所需的训练样本、检验样本和测试样本,在遵循BP网络建模基本原则和步骤的情况下,建立了可靠、有效的网络安全综合评价模型。16个实例研究表明：提出的样本生成方法、建模过程是可靠的,并能有效地避免出现“过训练”和“过拟合”现象,建立的BP模型具有较好的泛化能力,不受人为因素的影响,各评价指标与网络安全等级之间存在明显的非线性关系,网络安全策略对网络安全的影响最大。     

Using the artificial neural network model for modeling the performance of the counter flow vortex tube

In this study, the effect of the nozzle number and the inlet pressure on the heating and cooling performance of the counter flow type vortex tube has been modeled with artificial neural networks (ANN) by using the experimentally obtained data. ANN has been designed by Pithiya software. In the developed system output parameter temperature gradient between the cold and hot outlets (ΔT) has been determined using inlet parameters such as the inlet pressure (P_inlet), nozzle number (N), and cold mass fraction (μ_c). The back-propagation learning algorithm with variant which is Levenberg–Marquardt (LM) and Fermi transfer function have been used in the network. In addition, the statistical validity of the developed model has been determined by using the coefficient of determination (R²), the root means square error (RMSE) and the mean absolute percentage error (MAPE). R², RMSE and MAPE have been determined for ΔT as 0.9947, 0.188224, and 0.0460, respectively.     

An innovative method for dynamic update of initial water table in XXT model based on neural network technique

The initial subsurface flow of whole basin plays a quite important role in daily rainfall–runoff simulation. However, general physically based rainfall–runoff model, such as the XXT model (a hybrid model of TOPographic MODEL and the Xinanjiang model), is difficult to catch the non-linear factors and take full advantages of previous information of rainfall and runoff that is essential to the initial watershed average saturation deficit of each time step. In order to address the issue, this study selected the initial subsurface flow for the whole time series of the XXT model as the breakthrough point, and used the observed runoff and rainfall data of two days before the present day as the inputs of artificial neural network (ANN) and initial subsurface flow of the present day as the output, then integrated ANN into runoff generation module of XXT model and finally tested the integrated model for daily runoff simulation in large-scale and semi-arid Linyi watershed, eastern China. In addition, this work employ particle swarm optimization (PSO) algorithm to seek the best combination of 6 physical parameters in XXT and a great number of weights in ANN to avoid the local optimization. The results show that the integrated model performs much better than XXT in terms of Nash–Sutcliffe efficiency coefficient (NE) and root mean square error (RMSE). Hence, the new integrating approach proposed here is promising for daily rainfall–runoff modeling and can be easily extended to other process-based models.     

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.     

人工神经网络泛化问题研究综述*

从理论、方法（思想）和技术三个层次回顾了以往工作,讨论了模型复杂度、样本复杂度及两者之间关系的相关研究;在实际中,通过控制模型复杂度、调整样本等具体技术可以在一定程度上提高神经网络的泛化能力,但这些技术仍然存在一些问题没有解决。最后提出了对今后研究的展望。     

用神经网络实现一般访问结构的多重秘密共享

利用人工神经网络,结合RSA密码体制,实现了一种基于一般访问结构的多重秘密共享方案.在该方案中,秘密份额是人工神经网络收敛结果,各参与者共享多个秘密只需要维护一个秘密份额.共享多个秘密只需要进行一次人工神经网络训练,从而提高了方案的效率;在秘密分发和恢复时,利用RSA密码体制保证方案的安全性和正确性.分析表明,该方案是一个安全的、实用的秘密共享方案.     

An artificial neural network model of LRU-cache misses on out-of-order embedded processors

For in-order processors, the stack distance theory is a well-known means to fast model LRU-cache behaviors . However, it cannot be applied directly on out-of-order processors due to the changing of stack distance histograms by mechanisms such as reordering executions, speculative loads, load-in-store operations and non-blocking issues.This paper proposes an Artificial Neural Network (ANN) model to fast forecast private LRU-cache behaviors on out-of-order processors. To verify our model in real commercial applications, the evaluation scenarios chosen in this paper, not only include traditional embedded benchmark suits, such as Mibench 1.0 and Mediabench II, but also embrace Android applications from Mobybench 2.0 benchmark suit as well.Compared with results from Gem5 simulations, the average root mean square error of our ANN model is less than 6% with the prediction speed increasing about 2.5× –3×.     

采用附加动量法的BP技术进行脱硫可行性数学模型的研究

控制SO2污染是当今世界关心的重大课题。煤是中国能源支柱，由燃煤造成的硫污染尤为突出。研究煤中硫分与产率之间的关系并建立适用的 模型是脱除硫工艺中至关重要的一步。本研究利用神经网络技术来研究硫分与产率之间的关系模型，此研究为寻求一种技术上可行、经济上合理的脱硫工艺，进而减少硫污染具有重要的理论价值和实际应用价值。     

An artificial neural network model for estimating crop yields using remotely sensed information

Crop yield forecasting is a very important task for researchers in remote sensing. Problems exist with traditional statistical modelling (especially regression models) of nonlinear functions with multiple factors in the cropland ecosystem. This paper describes the successful application of an artificial neural network in developing a model for crop yield forecasting using back-propagation algorithms. The model has been adapted and calibrated using on the ground survey and statistical data, and it has proven to be stable and highly accurate.     

一种网络流量预测的小波神经网络模型

结合小波变换和人工神经网络的优势,建立一种网络流量预测的小波神经网络模型。首先对流量时间序列进行小波分解,得到小波变换尺度系数序列和小波系数序列,以系数序列和原来的流量时间序列分别作为模型的输入和输出,构造人工神经网络并且加以训练。用实际网络流量对该模型进行验证,结果表明,该模型具有较高的预测效果。     

互感器故障诊断人工神经网络法

基于神经网络的基本结构和算法，该文建立了一个用于高压电磁式互感器故障诊断的人工神经网络。其中采用了有效的网络学习算法，旨在全面、快速和准确地实现互感器故障诊断，以提高互感器及电力系统运行的可靠性。根据互感器的故障特征，该文建立一个3层前向神经网络，采用误差逆传播学习算法进行了讨论，并由仿真计算结果加以论证。     

基于ANN端元估计的高光谱图像解混算法

针对高光谱图像解混问题进行研究,发现传统解混算法在保持端元数目不变的情况下,得到的解混精度不高。为此,基于人工神经网络（ANN）提出一种估计单像素点中端元数目和类别的解混算法。首先利用人工神经网络对遥感图像中各个像素的端元数目和类别进行估计;之后依据估计结果确定解混算法的目标函数,并引入改进的差分搜索算法对目标函数进行优化求解;最终获取地物丰度和待求参数,实现高光谱图像的解混。仿真数据和真实遥感数据实验表明,与现有的解混算法相比,所提解混算法具有更高的解混性能,更加符合实际场景的情况。     

Prediction approach of software fault-proneness based on hybrid artificial neural network and quantum particle swarm optimization

The identification of a module's fault-proneness is very important for minimizing cost and improving the effectiveness of the software development process. How to obtain the correlation between software metrics and module's fault-proneness has been the focus of much research. This paper presents the application of hybrid artificial neural network (ANN) and Quantum Particle Swarm Optimization (QPSO) in software fault-proneness prediction. ANN is used for classifying software modules into fault-proneness or non fault-proneness categories, and QPSO is applied for reducing dimensionality. The experiment results show that the proposed prediction approach can establish the correlation between software metrics and modules’ fault-proneness, and is very simple because its implementation requires neither extra cost nor expert's knowledge. Proposed prediction approach can provide the potential software modules with fault-proneness to software developers, so developers only need to focus on these software modules, which may minimize effort and cost of software maintenance.     

Using Boolean factors for the construction of an artificial neural networks

ABSTRACT

We propose a novel approach to define Artificial Neural Network(ANN) architecture from Boolean factors. ANNs are a subfield of machine learning applicable to several areas of life. However, defining its architecture for solving a given problem is not formalized and remains an open research problem. Since it is difficult to look into the network and figure out exactly what it has learnt, the complexity of such a technique makes its interpretation more tedious. We propose in this paper to build feedforward ANNs using the optimal factors obtained from the Boolean context representing a data. Since optimal factors completely cover the data and therefore give an explanation to these data, We could give an interpretation to the neurons activation and justify the presence of a neuron in our proposed neural network. We show through experiments and comparisons on the use data sets that this approach provides relatively better results for some key performance measures.     

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.     

Application of a radial basis function artificial neural network to seismic data inversion

We investigate here the performance and the application of a radial basis function artificial neural network (RBF-ANN) type, in the inversion of seismic data. The proposed structure has the advantage of being easily trained by means of a back-propagation algorithm without getting stuck in local minima. The effects of network architectures, i.e. the number of neurons in the hidden layer, the rate of convergence and prediction accuracy of ANN models are examined. The optimum network parameters and performance were decided as a function of testing error convergence with respect to the network training error. An adequate cross-validation test is run to ensure the performance of the network on new data sets. The application of such a network to synthetic and real data shows that the inverted acoustic impedance section was efficient.     

An artificial neural network noise reduction method for chaotic attractors

A method is presented to reduce noise in chaotic attractors without knowing the underlying maps. The method is based on using Artificial Neural Network (ANN) for moderate levels of additive noise. For high levels of additive noise, a combination of a refinement procedure with ANN is used. In this case, only one refinement is needed for the successful use of ANN. The obtained ANN model is used for long-term predictions of the future behavior of a Henon attractor, using information based only on past values.     

An artificial neural network for nonlinear estimation of the turbine flow-meter coefficient

Turbine flow meters find various applications in the process industries, such as batch control, measuring fuel oil and gas consumption, controlling blending processes, etc. The turbine meter is a rotor driven by the fluid being metered, at a speed proportional to the flow rate.The actual behavior of a turbine flow meter is a complex function of many variables; among these are the temperature, pressure, and viscosity of the fluid; the lubricating qualities of the fluid; bearing wear; and environmental factors. The turbine meter coefficient is referred to as the ‘K factor’, and is defined as the number of pulses per unit volume. At present, there is no single mathematical equation to predict the actual K factor. More accurate estimations and trending of the K factor will not only facilitate preventive maintenance, replacement analysis, etc., but will also ensure that material flow accounting is accurate.This research explores the use of neural-network models to aid in the estimation of the actual K factor that reflects the effect of the actual operating conditions of the turbine meter. This research analyzed data from three different turbine flow meters measuring the rate of pumping oil from the North Sea, for a company that operates off-shore oil platforms. The use of neural networks presents a new approach to the capturing of the underlying nonlinear relationships among the various input variables and the K factor. The results from this study report significant percentage reductions in mean absolute errors for the neural-network predictions over the company’s present estimation practices for the turbine flow-meter coefficient.     

用于构建人工大脑的神经网络模型的分析与评估

目前人工脑的研究还处于起步阶段,构造智能化人工脑的方法正在探索中.影响人工脑性能的关键部分在于所选用的人工神经网络,针对目前已提出的三个网络模型,即CoDi模型、TiPo模型和DePo模型,进行了评估研究.采用的评估方法是通过解决曲线跟踪问题对模型进行测试.测试结果显示DePo模型曲线跟踪取得的效果较另两个更好,TiPo模型跟CoDi模型的性能相似.人工脑的进一步研究工作将包括提出更接近生物机制的模型或工程角度更有进化能力的模型.