Short-term ozone forecasting by artificial neural networks

In this work we report preliminary results of a study aiming to develop an intelligent tool for performing ozone forecasting in the polluted atmosphere of México City. This tool is based in the paradigm of neural networks. Two neural models are used in this work, namely, the Bidirectional Associative Memory (BAM) and the Holographic Associative Memory (HAM). We analyse and preprocess daily patterns of meteorological variables and concentrations of pollutants as measured by five monitoring stations in México City. These patterns are used to train both neural networks and then we use them to predict ozone at one point in the city. Preliminary results are reported and some conclusions are drawn.     

Prediction of feed abrasive value by artificial neural networks and multiple linear regression

In order to evaluate rapid testing methods based on the relationship between feed abrasive value (FAV) and physicochemical properties (particle size, bulk density, dry matter (DM), soluble dry matter, water-holding capacity (WHC), ash, crud protein, neutral detergent fiber (NDF), physically effective NDF and non-fibrous carbohydrates (NFC)), 100 empirical dataset were used. Relationships were investigated using multiple linear regression (MLR) and artificial neural networks (ANNs). The mean relative error was significantly (P?<?0.01) lower for ANN than MLR model. Globally, the non-linear ANN model approach is shown to provide a better prediction of FAV than linear multiple regression.     

A novel hybrid classification model of artificial neural networks and multiple linear regression models

The classification problem of assigning several observations into different disjoint groups plays an important role in business decision making and many other areas. Developing more accurate and widely applicable classification models has significant implications in these areas. It is the reason that despite of the numerous classification models available, the research for improving the effectiveness of these models has never stopped. Combining several models or using hybrid models has become a common practice in order to overcome the deficiencies of single models and can be an effective way of improving upon their predictive performance, especially when the models in combination are quite different. In this paper, a novel hybridization of artificial neural networks (ANNs) is proposed using multiple linear regression models in order to yield more general and more accurate model than traditional artificial neural networks for solving classification problems. Empirical results indicate that the proposed hybrid model exhibits effectively improved classification accuracy in comparison with traditional artificial neural networks and also some other classification models such as linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), K-nearest neighbor (KNN), and support vector machines (SVMs) using benchmark and real-world application data sets. These data sets vary in the number of classes (two versus multiple) and the source of the data (synthetic versus real-world). Therefore, it can be applied as an appropriate alternate approach for solving classification problems, specifically when higher forecasting accuracy is needed.     

基于人工神经网络实现的QoS路由决策

讨论了基于神经网络自学习算法实现QoS路由决策的问题。为了证明利用人工神经网络优化路由决策的可行性,在由17台服务器（节点）搭建的实验网络环境中,每个节点上均设计了由几个神经元组成的神经网络,各神经元依据网络的测量数据,通过学习算法动态地进行路由决策。实验结果表明,在以最小跳转数或最小延时为QoS目标时,神经网络所提供的路由决策均可以有效地使QoS接近最优值;同时,当神经网络综合考虑延时和最小跳转数两项QoS指标时,网络延时状况要优于只考虑一项指标的情况。实验结果证明了利用神经网络在节点上进行分布式的路由     

Predicting river daily flow using wavelet-artificial neural networks based on regression analyses in comparison with artificial neural networks and support vector machine models

This study investigates the ability of wavelet-artificial neural networks (WANN) for the prediction of short-term daily river flow. The WANN model is improved by conjunction of two methods, discrete wavelet transform and artificial neural networks (ANN) based on regression analyses, respectively. The proposed WANN models are applied to the daily flow data of Vanyar station, on the Ajichai River in the northwest region of Iran, and compared with the ANN and support vector machine (SVM) techniques. Mean square error (MSE), mean absolute error (MAE) and correlation coefficient (R) statistics are used for evaluating precision of the WANN, ANN and SVM models. Comparison results demonstrate that the WANN model performs better than the ANN and SVM models in short-term (1-, 2- and 3-day ahead) daily river flow prediction.

     

Application of artificial neural networks in linear profile monitoring

In many quality control applications the quality of process or product is characterized and summarized by a relation (profile) between a response variable and one or more explanatory variables. Such profiles can be modeled using linear or nonlinear regression models. In this paper we use artificial neural networks to detect and classify the shifts in linear profiles. Three monitoring methods based on artificial neural networks are developed to monitor linear profiles. Their efficacies are assessed using average run length criterion.     

Assessment and prediction of tropospheric ozone concentration levels using artificial neural networks

This work deals specifically with the use of a neural network for ozone modelling in the lower atmosphere. The development of a neural network model is presented to predict the tropospheric (surface or ground) ozone concentrations as a function of meteorological conditions and various air quality parameters. The development of the model was based on the realization that the prediction of ozone from a theoretical basis (i.e. detailed atmospheric diffusion model) is difficult. In contrast, neural networks are useful for modelling because of their ability to be trained using historical data and because of their capability for modelling highly non-linear relationships. The network was trained using summer meteorological and air quality data when the ozone concentrations are the highest. The data were collected from an urban atmosphere. The site was selected to represent a typical residential area with high traffic influences. Three neural network models were developed. The main emphasis of the first model has been placed on studying the factors that control the ozone concentrations during a 24-hour period (daylight and night hours were included). The second model was developed to study the factors that regulate the ozone concentrations during daylight hours at which higher concentrations of ozone were recorded. The third model was developed to predict daily maximum ozone levels. The predictions of the models were found to be consistent with observations. A partitioning method of the connection weights of the network was used to study the relative percent contribution of each of the input variables. The contribution of meteorology on the ozone concentration variation was found to fall within the range 33.15–40.64%. It was also found that nitrogen oxide, sulfur dioxide, relative humidity, non-methane hydrocarbon and nitrogen dioxide have the most effect on the predicted ozone concentrations. In addition, temperature played an important role while solar radiation had a lower effect than expected. The results of this study indicate that the artificial neural network (ANN) is a promising method for air pollution modelling.     

基于人工神经网络的智能诊断系统(NNIDS)

本文论述了一个基于人工神经网络 (ArtificialNeuralNetworks)的智能诊断系统 (NNIDS)的设计思想、总体结构及实现的基本原理 ,为解决智能诊断问题提供了一种有效途径。系统具有知识自动获取、识别速度快、橹棒性及容错能力强等特点。     

Optimal software release scheduling based on artificial neural networks

The determination of the optimal software release schedule plays an important role in supplying sufficiently reliable software products to actual market or users. In the existing methods, the optimal software release schedule was determined by assuming the stochastic and/or statistical model called software reliability growth model. In this paper, we propose a new method to estimate the optimal software release timing which minimizes the relevant cost criterion via artificial neural networks. Recently, artificial neural networks are actively studied with many practical applications and are applied to assess the software product reliability. First, we interpret the underlying cost minimization problem as a graphical one and show that it can be reduced to a simple time series forecasting problem. Secondly, artificial neural networks are used to estimate the fault-detection time in future. In numerical examples with actual field data, we compare the new method based on the neural networks with existing parametric methods using some software reliability growth models and illustrate its benefit in terms of predictive performance. A comprehensive bibliography on the software release problem is presented. This revised version was published online in June 2006 with corrections to the Cover Date.     

基于神经网络分类的图像水印算法

将人工神经网络（ANN）、广义猫映射及概率统计等知识相结合构造了一种图像空间域水印算法。采用神经网络作为载体图像的纹理分类器,突出原始图像的纹理区。使用广义猫映射对水印进行置乱预处理,提高了水印信息的安全性。水印嵌入时采用最小化像素改变的优化策略,提取时应用概率统计等知识较好地实现了水印信息不可见性和鲁棒性的统一。实验结果表明,该方法能有效地抵抗剪切攻击、噪声攻击、最低有效位（LSB）攻击、滤波攻击等。     

A system to diagnose atherosclerosis via wavelet transforms,principal component analysis and artificial neural networks

In this study, Doppler ultrasound signals were acquired from carotid arteries of 82 patients with atherosclerosis and 95 healthy volunteers. We have employed discrete wave transform (DWT) of Doppler signals and power spectral density graphics of these decomposed signals using Welch method. After that, we have performed Principal component analysis (PCA) for data reduction and ANN in order to distinguish between atherosclerosis and healthy subjects.After the training phase, testing of the artificial neural network (ANN) was established. The overall results show that 97.9% correct classification was achieved, whereas two false classifications have been observed for the test group of 97 people.In conclusion we are proposing a complimentary expert system that can be coupled to software of the ultrasonic Doppler devices. The diagnosis performances of this study show the advantages of this system: it is rapid, easy to operate, noninvasive, inexpensive and making a decision without hesitation.     

Multiple models switching control based on recurrent neural networks

This paper presents a novel approach in designing adaptive controller to improve the transient performance for a class of nonlinear discrete-time systems under different operating modes. The proposed scheme consists of generalized minimum variance (GMV) controllers and a compensating controller. GMV controllers are based on the known nominal linear multiple models, while the compensating controller is based upon a recurrent neural network. The adaptation law of network weight is derived from Lyapunov stability theory. A suitable switching control strategy is applied to choose the best controller by the performance indices at every sampling instant. Simulations are discussed in order to illustrate the merits of the proposed method.     

基于人工神经网络和遗传算法的网络攻击检测

针对网络攻击检测准确率较低的问题,提出基于人工神经网络和遗传算法的混合网络攻击检测算法.将多目标遗传算法和多项式逻辑回归模型组合成封装特征选择算法,利用多项式回归模型对多分类数据的高效学习能力以及多目标遗传算法的全局优化能力,提取数据的最优特征子集;将降维后的特征集送入感知机训练,利用重引力搜索算法搜索神经网络的参数....     

Rotating machine fault detection based on HOS and artificial neural networks

In order to identify the faults of rotating machinery, classification process can be divided into two stages: one is the signal preprocessing and the feature extraction; the other is the recognition process. In the preprocessing and feature extraction stage, the higher-order statistics (HOS) is used to extract features from the vibration signals. In the recognition process, two kinds of neural network classifier are used to evaluate the classification results. These two classifiers are self-organizing feature mapping (SOM) network for collecting data at the initial stage and learning vector quantization (LVQ) network at the identification stage. The experimental results obtained from HOS as preprocessor to extract the features of fault are clearer than those obtained from the power spectrum. In addition, the recognizable rate by using either SOM or LVQ as classifiers is 100%.     

基于VLRBP神经网络的汇率预测

为了提高汇率预测的准确性,分别使用VLRBP神经网络模型和GRNN模型及ARIMA模型对欧元汇率时间序列进行建模和预测,通过实证分析发现基于VLRBP的神经网络对于含有大量非线性成分的欧元汇率时间序列的预测比较准确.在分析了最速下降BP学习算法的缺点后,提出利用VLRBP学习算法来解决神经网络振荡和收敛速度过慢的缺陷,并取得较好的效果.同时,为了提高VLRBP网络的泛化性能,提出在训练VLRBP神经网络时应用浴盆曲线方法选取隐层神经元个数和滑动窗口尺寸,试验结果表明该方法适合神经网络模型.     

利用人工鱼群算法优化前向神经网络

人工鱼群算法(AFSA)是一种最新提出的新型的寻优策略，文中尝试将人工鱼群算法用于三层前向神经网络的训练过程，建立了相应的优化模型，进行了实际的编程计算，并与加动量项的BP算法、演化算法以及模拟退火算法进行比较，结果表明AFSA具有鲁棒性强，全局收敛性好，以及对初值的不敏感性等特点。     

Remote health monitoring adoption model based on artificial neural networks

The purpose of this research is to utilize the adoption model of remote health monitoring established by artificial neural networks (ANNs). The adoption model by the naming is the healthcare information adoption model (HIAM) that it is created first time by myself. The HIAM focused on citizens in Taiwan as research subjects. The main research result showed that people’s perceived usefulness and benefits (PUB) must be raised in order to effectively increase the adoption of remote health monitoring. Moreover, this research has proved that the utilization of the adoption model of remote health monitoring established by ANN based on the HIAM is feasible. These findings may offer significant reference for subsequent studies.     

主成分分析与神经网络的结合在多变量序列预测中的应用

目前预测方法的研究主要集中在单变量时间序列上,本文建立起一种针对多元变量非线性时间序列建模和预测的方法框架.首先,同时考虑序列状态间的线性相关性和非线性相关性,建立初始延迟窗以包含充分的预测信息;然后,利用主成分分析(PCA)方法寻找不同变量在数据空间中的最大方差方向,扩展PCA应用于提取多个变量的综合信息,重构多元变量输入状态相空间;最后,利用神经网络逼近不同变量之间以及当前状态和将来状态之间的函数映射关系,实现多元变量预测.对Ro¨ssler混沌方程和大连降雨、气温序列的预测仿真说明了本文方法的有效性,为多元变量时间序列分析提供了一条新的途径.     

直链聚合物的结构性质关系的人工神经网络建模

应用人工神经网络构造了2个直链聚合物的结构性质关系模型。一个是直链聚合物的基团均值法描述的结构参数与其12种性质间定量关系的模型（模型1A）；一个是直链聚合物的连接性指数描述的结构参数与其12种性质间定量关系的模型（模型2A）。讨论了2个模型的参数设置，而2个模型给出的聚合物的12种性质的拟合误差（拟合值与实验值间的标准偏差）分别是：V（298K）为18.9（模型1A）/40.5(模型2A）cc/mole,Ecoh为8.019/11.122KJ/mole,δ为0.74/2.17(J/cc)^0.5,Fd为228/235J0.5cm^1.5/mole,Tg为27/52K,Ps为25/37（cc/mole)(dyn/cm)^1/4,n为0.0140/0.5191,ζ为7.45/5.3610^-6cc/mole,UR为727/593cm^10/3(sec^1/3mole),UH为568/674cm^10/3(sec^1/3mole),Hμsum为649/719gJ^1/3mole^4/3,Yd,1/2为10.6/10.5K*kg/mole。结果表明，所建立的模型可用于直链聚合物性质的预测，而人工神经网络确实是聚合物结构性质关系研究中的一个有利的数学工具。     

基于改进AFSA算法的BP神经网络的研究

针对BP神经网络收敛速度慢、易陷入局部极小的缺点,提出将改进的人工鱼群算法与BP算法相结合的混合算法训练人工神经网络,建立了相应的优化训练模型及训练过程.通过基于生物免疫机制改进的人工鱼群算法优化训练多层前向神经网络,使神经网络对训练初值和参数要求不高,扩大了权值的搜索空间,提高了收敛速度和学习精度,有效地协调全局和局部搜索能力.仿真结果表明,该算法性能优于其它算法,具有均方误差值小,收敛速度快和计算精度高等特点,是一种更有效的神经网络训练算法.