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。     

神经网络在洪水预测中的应用研究

对基于神经网络的洪水序列预测方法进行了研究.将动态学习率、惯性冲量方法改进的神经网络模型用于水文时间序列洪水预报中,提出以确定性系数最大为评价标准的参数优选方法.经两个洪水序列的实例研究结果表明,神经网络对于变化平缓的洪水序列,预报效果很好,对于彼动剧烈的复杂水文序列,洪水预报效果不如前者.     

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。     

多层神经网络在入侵检测中的应用

对多层ANN的结构和向后传播算法进行了设计,提出了移动窗口和事件子视图等概念,通过提取审计事件类型的方法,采样了ANN的训练数据和测试数据.具体实现了设计算法,并用该软件分别对UNIX和Windows XP两个操作系统的数据进行了实验.实验结果表明,多层ANN可以作为一个入侵检测的模型和技术应用于入侵检测之中.     

Application of an artificial neural network in canopy scattering inversion

Owing to their recent success in other inversion tasks, application of an artificial neural network to the development of an inversion algorithm for radar scattering from vegetation canopies is considered. Because canopy scattering models are complicated functions of the desired biophysical parameters (vegetation biomass, leaf area index, soil moisture content, etc.), the development of an effective inversion algorithm is not a straightforward task. The Michigan Microwave Canopy Scattering (MIMICS) model, which has shown remarkable success in predicting the radar response to vegetation canopies, was used, as were measured polarimetric backscatter values. Hence, the radiative transfer simulation code, MIMICS, was used to produce some of the training data. The inputs to the neural network were the expected polarimetric backscatter values from specific canopies, while the outputs were the desired parameters, such as tree heights, crown thickness, leaf density, etc.

Two special cases were examined: (1) inversion of MIMICS given modelled aspen stands of different ages; (2) inversion of measured data from the Duke forest loblolly pine stands. The MIMICS inversion shows that neural networks are capable of accurately inverting some of the parameters of such a complicated model. The implication is that once MIMICS is made to model the radar data for a specific application, then inversion of the radar data may be accomplished. The measured data inversion shows that, even without a model such as MIMICS, one may train a neural network to invert several parameters of interest. However, this depends on accurate and complete surveys of the ground truth data to be useful.     

改进的人工蜂群算法在神经网络中的应用

人工蜂群算法是模拟蜜蜂采蜜行为而提出的一种新的启发式仿生算法,属于典型的群体智能算法。提出了一种改进的人工蜂群算法,并利用改进后的人工蜂群算法来优化传统BP算法（神经网络算法中的误差方向传播算法）中网络参数的权值。实验结果证明该优化算法提高了BP神经网络收敛解的精度,加快了BP神经网络收敛速度。     

Application of an artificial neural network to improve short-term road ice forecasts

This paper describes how a three-layer artificial neural network (NN) can be used to improve the accuracy of short-term (3–12 hours) automatic numerical prediction of road surface temperature, in order to cut winter road maintenance costs, reduce environmental damage from oversalting and provide safer roads for road users. In this paper, the training of the network is based on historical and preliminary meteorological parameters measured at an automatic roadside weather station, and the target of the training is hourly error of original numerical forecasts. The generalization of the trained network is then used to adjust the original model forecast. The effectiveness of the network in improving the accuracy of numerical model forecasts was tested at 39 sites in eight countries. Results of the tests show that the NN technique is able to reduce absolute error and root-mean-square error of temperature forecasts by 9.9–29%, and increase the accuracy of frost/ice prediction.     

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.     

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.     

Application of artificial neural network models to linear and nonlinear RF circuit modeling

Typical RF and wireless circuits comprise a large number of linear and nonlinear components. The complexity of the RF portion of a wireless system continues to increase in order to support multiple standards, multiple frequency bands, the need for higher bandwidth, and stringent adjacent channel specifications. The time required to carry out a virtual prototyping of such complex circuits and their trade‐off analysis with the baseband circuitry can be unacceptably long, because both the circuit simulation and optimization procedures can be very time consuming. Typically, one divides the task into those of designing the nonlinear elements or subcircuits that can be accurately analyzed by using RF simulators, and uses circuit level analysis for simulating the circuits at module level. In this article, we will review some approaches to modeling both the linear RF elements as well as nonlinear subcircuits (amplifiers, mixers, VCOs), and will emphasize on the application of the artificial neural networks (ANNs). Furthermore, we will demonstrate the use of the ANN to the design of RF circuits and illustrate their application to wireless types of problems of practical interest. © 2001 John Wiley & Sons, Inc. Int J RF and Microwave CAE 11: 231–247, 2001.     

pH中和系统辨识中的人工神经网络应用

pH中和作为化工、生物、发电和污水处理中的一个重要过程,具有极强的非线性和不确定性,很难对其进行精确建模,因此,pH值的控制一直是工业过程控制中的一个难题。本文借鉴了计算机领域中神经网络(NN)在非线性系统建模中的显著作用,结合对pH中和过程机理的分析,建立了基于BP神经网络的辨识模型,对典型的pH中和过程系统辨识进行了仿真研究,并进行了相关试验。试验结果表明:神经网络在pH中和过程辨识中具有较高的辨识精度,有着广阔的应用前景。     

An artificial neural network approach to the problem of wireless sensors network localization

One of the imperative problems in the realm of wireless sensor networks is the problem of wireless sensors localization. Despite the fact that much research has been conducted in this area, many of the proposed approaches produce unsatisfactory results when exposed to the harsh, uncertain, noisy conditions of a manufacturing environment. In this study, we develop an artificial neural network approach to moderate the effect of the miscellaneous noise sources and harsh factory conditions on the localization of the wireless sensors. Special attention is given to investigate the effect of blockage and ambient conditions on the accuracy of mobile node localization. A simulator, simulating the noisy and dynamic shop conditions of manufacturing environments, is employed to examine the neural network proposed. The neural network performance is also validated through some actual experiments in real-world environment prone to different sources of noise and signal attenuation. The simulation and experimental results demonstrate the effectiveness and accuracy of the proposed methodology.     

人工神经网络用于乙醇发酵生产过程的建模探讨

基于发酵生产的特点及建模要求,以某企业燃料乙醇生产过程为研究对象,利用工业生产中的参数及数据,建立了以乙醇发酵效果为目标的BP神经网络模型,以静态模型反映复杂的动态问题.探讨了乙醇发酵生产模型的误差产生原因,并提出改进方案,根据已有经验将相关参数进行了合理组合,调整神经网络模型的输入输出参数结构,以提高仿真模拟效果.通过多次模型改进,使模拟的平均相对误差从10%提高至5.4%,为进一步研究发酵生产建模提供了思路.     

Application of artificial neural network for determination of standard time in machining

The purpose of this article is to present the application of neural network for time per unit determination in small lot production in machining. A set of features considered as input vector and time consumption in manufacturing process was presented and treated as output of the neural net. A neural network was used as a machining model. Sensitivity analysis was made and proper topology of neural network was determined.     

Pipelined functional link artificial recurrent neural network with the decision feedback structure for nonlinear channel equalization

This paper presents a computationally efficient nonlinear adaptive filter by a pipelined functional link artificial decision feedback recurrent neural network (PFLADFRNN) for the design of a nonlinear channel equalizer. It aims to reduce computational burden and improve nonlinear processing capabilities of the functional link artificial recurrent neural network (FLANN). The proposed equalizer consists of several simple small-scale functional link artificial decision feedback recurrent neural network (FLADFRNN) modules with less computational complexity. Since it is a module nesting architecture comprising a number of modules that are interconnected in a chained form, its performance can be further improved. Moreover, the equalizer with a decision feedback recurrent structure overcomes the unstableness thanks to its nature of infinite impulse response structure. Finally, the performance of the PFLADFRNN modules is evaluated by a modified real-time recurrent learning algorithm via extensive simulations for different linear and nonlinear channel models in digital communication systems. The comparisons of multilayer perceptron, FLANN and reduced decision feedback FLANN equalizers have clearly indicated the convergence rate, bit error rate, steady-state error and computational complexity, respectively, for nonlinear channel equalization.     

Application of an artificial immune system-based fuzzy neural network to a RFID-based positioning system

Due to the rapid development of globalization, which makes supply chain management more complicated, more companies are applying radio frequency identification (RFID), in warehouse management. The obvious advantages of RFID are its ability to scan at high-speed, its penetration and memory. In addition to recycling, use of a RFID system can also reduce business costs, by indentifying the position of goods and picking carts. This study proposes an artificial immune system (AIS)-based fuzzy neural network (FNN), to learn the relationship between the RFID signals and the picking cart’s position. Since the proposed network has the merits of both AIS and FNN, it is able to avoid falling into the local optimum and possesses a learning capability. The results of the evaluation of the model show that the proposed AIS-based FNN really can predict the picking cart position more precisely than conventional FNN and, unlike an artificial neural network, it is much easier to interpret the training results, since they are in the form of fuzzy IF–THEN rules.     

Application of an artificial immune system-based fuzzy neural network to a RFID-based positioning system

Due to the rapid development of globalization, which makes supply chain management more complicated, more companies are applying radio frequency identification (RFID), in warehouse management. The obvious advantages of RFID are its ability to scan at high-speed, its penetration and memory. In addition to recycling, use of a RFID system can also reduce business costs, by indentifying the position of goods and picking carts. This study proposes an artificial immune system (AIS)-based fuzzy neural network (FNN), to learn the relationship between the RFID signals and the picking cart’s position. Since the proposed network has the merits of both AIS and FNN, it is able to avoid falling into the local optimum and possesses a learning capability. The results of the evaluation of the model show that the proposed AIS-based FNN really can predict the picking cart position more precisely than conventional FNN and, unlike an artificial neural network, it is much easier to interpret the training results, since they are in the form of fuzzy IF–THEN rules.     

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.     

神经网络模式识别技术在化学侦检领域的应用

介绍了神经网络技术的研究现状,特别是在化学领域的应用情况,以及反向传播人工神经网络(BP-ANN)的基本原理和算法.结合波谱解析和化学传感器阵列两个方面对神经网络模式识别技术在化学毒剂侦检领域的应用特点进行了分析.指明了ANN模式识别技术在应用中存在的问题,并对该方向今后的发展提出了建议.