Predicting freeway pavement construction cost using a back-propagation neural network : a case study in Henan, China

The objective of this research was to develop a model to estimate future freeway pavement construction costs in Henan Province, China. A comprehensive set of factors contributing to the cost of freeway pavement construction were included in the model formulation. These factors comprehensively reflect the characteristics of region and topography and altitude variation, the cost of labour, material, and equipment, and time-related variables such as index numbers of labour prices, material prices and equipment prices. An Artificial Neural Network model using the Back-Propagation learning algorithm was developed to estimate the cost of freeway pavement construction. A total of 88 valid freeway cases were obtained from freeway construction projects let by the Henan Transportation Department during the period 1994−2007. Data from a random selection of 81 freeway cases were used to train the Neural Network model and the remaining data were used to test the performance of the Neural Network model. The tested model was used to predict freeway pavement construction costs in 2010 based on predictions of input values. In addition, this paper provides a suggested correction for the prediction of the value for the future freeway pavement construction costs. Since the change in future freeway pavement construction cost is affected by many factors, the predictions obtained by the proposed method, and therefore the model, will need to be tested once actual data are obtained.

Retention modeling and optimization of pH value and solvent composition in HPLC using back-propagation neural networks and uniform design

A novel method for the optimization of pH value and composition of mobile phase in HPLC using artificial neural networks and uniform design is proposed. As the first step. seven initial experiments were arranged and run according to uniform design. Then the retention behavior of the solutes is modeled using back-propagation neural networks. A trial method is used to ensure the predicting capability of neural networks. Finally, the optimal separation conditions can be found according to a global resolution function. The effectiveness of this method is validated by optimization of separation conditions for both basic and acidic samples.

An in silico approach for screening flavonoids as P-glycoprotein inhibitors based on a Bayesian-regularized neural network

P-glycoprotein (P-gp), an ATP-binding cassette (ABC) transporter, functions as a biological barrier by extruding cytotoxic agents out of cells, resulting in an obstacle in chemotherapeutic treatment of cancer. In order to aid in the development of potential P-gp inhibitors, we constructed a quantitative structure-activity relationship (QSAR) model of flavonoids as P-gp inhibitors based on Bayesian-regularized neural network (BRNN). A dataset of 57 flavonoids collected from a literature binding to the C-terminal nucleotide-binding domain of mouse P-gp was compiled. The predictive ability of the model was assessed using a test set that was independent of the training set, which showed a standard error of prediction of 0.146 +/- 0.006 (data scaled from 0 to 1). Meanwhile, two other mathematical tools, back-propagation neural network (BPNN) and partial least squares (PLS) were also attempted to build QSAR models. The BRNN provided slightly better results for the test set compared to BPNN, but the difference was not significant according to F-statistic at p = 0.05. The PLS failed to build a reliable model in the present study. Our study indicates that the BRNN-based in silico model has good potential in facilitating the prediction of P-gp flavonoid inhibitors and might be applied in further drug design.

Electric load forecasting using a fuzzy ART&ARTMAP neural network

This work presents a neural network based on the ART architecture ( adaptive resonance theory), named fuzzy ART& ARTMAP neural network, applied to the electric load-forecasting problem. The neural networks based on the ARTarchitecture have two fundamental characteristics that are extremely important for the network performance ( stability and plasticity), which allow the implementation of continuous training. The fuzzy ART& ARTMAP neural network aims to reduce the imprecision of the forecasting results by a mechanism that separate the analog and binary data, processing them separately. Therefore, this represents a reduction on the processing time and improved quality of the results, when compared to the Back-Propagation neural network, and better to the classical forecasting techniques (ARIMA of Box and Jenkins methods). Finished the training, the fuzzy ART& ARTMAP neural network is capable to forecast electrical loads 24 h in advance. To validate the methodology, data from a Brazilian electric company is used. (C) 2004 Elsevier B.V. All rights reserved.

Bayesian neural network approach for determining the risk of re-intervention after endovascular aortic aneurysm repair

This article proposes a Bayesian neural network approach to determine the risk of re-intervention after endovascular aortic aneurysm repair surgery. The target of proposed technique is to determine which patients have high chance to re-intervention (high-risk patients) and which are not (low-risk patients) after 5 years of the surgery. Two censored datasets relating to the clinical conditions of aortic aneurysms have been collected from two different vascular centers in the United Kingdom. A Bayesian network was first employed to solve the censoring issue in the datasets. Then, a back propagation neural network model was built using the uncensored data of the first center to predict re-intervention on the second center and classify the patients into high-risk and low-risk groups. Kaplan-Meier curves were plotted for each group of patients separately to show whether there is a significant difference between the two risk groups. Finally, the logrank test was applied to determine whether the neural network model was capable of predicting and distinguishing between the two risk groups. The results show that the Bayesian network used for uncensoring the data has improved the performance of the neural networks that were built for the two centers separately. More importantly, the neural network that was trained with uncensored data of the first center was able to predict and discriminate between groups of low risk and high risk of re-intervention after 5 years of endovascular aortic aneurysm surgery at center 2 (p = 0.0037 in the logrank test).

Frequency response function based structural damage detection using artificial neural networks

Damage detection in structures has become increasingly important in recent years. While a number of damage detection and localization methods have been proposed, few attempts have been made to explore the structure damage with frequency response functions (FRFs). This paper illustrates the damage identification and condition assessment of a beam structure using a new frequency response functions (FRFs) based damage index and Artificial Neural Networks (ANNs). In practice, usage of all available FRF data as an input to artificial neural networks makes the training and convergence impossible. Therefore one of the data reduction techniques Principal Component Analysis (PCA) is introduced in the algorithm. In the proposed procedure, a large set of FRFs are divided into sub-sets in order to find the damage indices for different frequency points of different damage scenarios. The basic idea of this method is to establish features of damaged structure using FRFs from different measurement points of different sub-sets of intact structure. Then using these features, damage indices of different damage cases of the structure are identified after reconstructing of available FRF data using PCA. The obtained damage indices corresponding to different damage locations and severities are introduced as input variable to developed artificial neural networks. Finally, the effectiveness of the proposed method is illustrated and validated by using the finite element modal of a beam structure. The illustrated results show that the PCA based damage index is suitable and effective for structural damage detection and condition assessment of building structures.

Detecting Earnings Management Using Neural Networks

Detecting Earnings Management Using Neural Networks. Trying to balance between relevant and reliable accounting data, generally accepted accounting principles (GAAP) allow, to some extent, the company management to use their judgment and to make subjective assessments when preparing financial statements. The opportunistic use of the discretion in financial reporting is called earnings management. There have been a considerable number of suggestions of methods for detecting accrual based earnings management. A majority of these methods are based on linear regression. The problem with using linear regression is that a linear relationship between the dependent variable and the independent variables must be assumed. However, previous research has shown that the relationship between accruals and some of the explanatory variables, such as company performance, is non-linear. An alternative to linear regression, which can handle non-linear relationships, is neural networks. The type of neural network used in this study is the feed-forward back-propagation neural network. Three neural network-based models are compared with four commonly used linear regression-based earnings management detection models. All seven models are based on the earnings management detection model presented by Jones (1991). The performance of the models is assessed in three steps. First, a random data set of companies is used. Second, the discretionary accruals from the random data set are ranked according to six different variables. The discretionary accruals in the highest and lowest quartiles for these six variables are then compared. Third, a data set containing simulated earnings management is used. Both expense and revenue manipulation ranging between -5% and 5% of lagged total assets is simulated. Furthermore, two neural network-based models and two linear regression-based models are used with a data set containing financial statement data from 110 failed companies. Overall, the results show that the linear regression-based models, except for the model using a piecewise linear approach, produce biased estimates of discretionary accruals. The neural network-based model with the original Jones model variables and the neural network-based model augmented with ROA as an independent variable, however, perform well in all three steps. Especially in the second step, where the highest and lowest quartiles of ranked discretionary accruals are examined, the neural network-based model augmented with ROA as an independent variable outperforms the other models.

Use of Data Driven Techniques for Short Lead Time Streamflow Forecasting in Mahanadi basin

Streamflow forecasts at daily time scale are necessary for effective management of water resources systems. Typical applications include flood control, water quality management, water supply to multiple stakeholders, hydropower and irrigation systems. Conventionally physically based conceptual models and data-driven models are used for forecasting streamflows. Conceptual models require detailed understanding of physical processes governing the system being modeled. Major constraints in developing effective conceptual models are sparse hydrometric gauge network and short historical records that limit our understanding of physical processes. On the other hand, data-driven models rely solely on previous hydrological and meteorological data without directly taking into account the underlying physical processes. Among various data driven models Auto Regressive Integrated Moving Average (ARIMA), Artificial Neural Networks (ANNs) are most widely used techniques. The present study assesses performance of ARIMA and ANNs methods in arriving at one-to seven-day ahead forecast of daily streamflows at Basantpur streamgauge site that is situated at upstream of Hirakud Dam in Mahanadi river basin, India. The ANNs considered include Feed-Forward back propagation Neural Network (FFNN) and Radial Basis Neural Network (RBNN). Daily streamflow forecasts at Basantpur site find use in management of water from Hirakud reservoir. (C) 2015 The Authors. Published by Elsevier B.V.

红发夫酵母发酵虾青素的研究

红发夫酵母分离于北美西部高山地区和日本一些岛屿上落叶树的渗出液中，因其所产主要色素为在水产养殖、食品和医药工业有广阔应用前景的虾青素而成为研究的热点。本论文对红发夫酵母Phaffia rhodozyma 的生长特性、培养参数与培养基组分对生长和虾青素积累的影响及其优化、虾青素合成的调节控制、虾青素的提取测定及红发夫酵母耐高温菌种的诱变进行了系统的研究。 虾青素是红发夫酵母的胞内色素，要对其进行分析首先要对红发夫酵母进行破壁处理，实验发现二甲亚砜是最有效的破壁溶剂，用氯仿和丙酮可以有效地把类胡萝卜素从二甲亚砜破壁后的红发夫酵母细胞中提取出来。 在固定摇床转速为200 rpm，温度为20 ℃的条件下，当种龄为36 h，以10％的接种量接入装液量为30 mL的250 mL三角瓶，初始pH为5.5时最有利于红发夫酵母的生长及类胡萝卜素的合成。 本实验中红发夫酵母最佳利用碳、氮源分别为蔗糖和蛋白胨，但蛋白胨价格昂贵，不适宜作单一氮源，因此使用硫酸铵和酵母膏作为复合氮源。 本论文采用了BP神经网络结合遗传算法的方法来优化红发夫酵母的发酵培养基，得到红发夫酵母发酵培养基的最佳配比为：蔗糖45.10 g/L、硫酸铵3.00 g/L、硫酸镁0.80 g/L、磷酸二氢钾1.40 g/L、酵母膏3.00 g/L、氯化钙0.50 g/L，使用优化后的培养基发酵类胡萝卜素产量达到8.20 mg/L，干重达到9.47 g/L，类胡萝卜素的产量比起始培养基提高了95.90%，干重提高了89.40%。 从代谢途径出发对红发夫酵母合成虾青素调控调控，选择谷氨酸、乙醇、VB1作为添加剂，通过正交试验设计得出三者添加水平分别为0.2 g/L，0.1% (V/V)，10 mg/L时，类胡萝卜素产量提高了25.73%，达到了10.31mg/L。 通过上述优化培养，本论文中红发夫酵母的虾青素产量从1.33 mg/L提高到9.12 mg/L，产量提高了6.86倍；总类胡萝卜素产量从4.23 mg/L提高到10.31 mg/L，产量提高了2.44倍；细胞干重从5.00 g/L提高到11.35 g/L，提高了2.27倍，总体提高效果显著。 红发夫酵母属于中低温菌，本论文采用紫外复合诱变的方式，通过高温筛选，得到一株能在35 ℃下能生长的突变株，但所产类胡萝卜素中虾青素所占比例很小，可能是诱变改变了红发夫酵母的代谢途径，阻断了虾青素的合成。 Phaffia rhodozyma is a heterobasidiomyceteous yeast that was originally isolated from the slime fluxes of brich tree wounds in mountain regions of northern Japan and southern Alaska. Phaffia rhodozyma produces astaxanthin as its principal carotenoid pigment, which has potential applications in acquaculture, food and pharmaceutical industry. This paper researched ways to break cell, analysis of astaxanthin, characteristics of growth, culture parameters and the effects of components of medium on growth and astaxanthin formation , optimization of culture medium, control of astaxanthin synthesis and mutagenesis of Phaffia rhodozyma. It is necessary to disrupt the yeast cell for extracting astaxanthin considering the yeast accumulating carotenoids in cell. Dimethyisulphoxide was the most effective solvent for breaking the yeast cell; acetone and chloroform were effective to extract carotenoids out of the disrupted cell. The optimum pH for growth and carotenoids synthesis is 5.5, the optimum medium volume is 30 mL (in 250 mL flask), the optimum culture time of inoculum is 36 h, the optimum inoculum concentration is 10%. The research on culture medium showed: sucrose is the best one of 6 carbon sources for growth and astaxanthin synthesis. Peptone is the best nitrogen source for growth and astaxanthin synthesis. Uniform Design was used for trial design of the formula medium components, then back-propagation neural network was established to modeling the relationships between the carotenoid yield and the concentration of medium components. Genetic algorithm (GA) was used for global optimization of the model. The optimum combination of the medium was obtained: sucrose 45.10 g/L, ammonium sulfate 3.00 g/L, magnesium sulfate 0.80 g/L, potassium dihydrogen phosphate 1.40 g/L, yeast extract 3.00 g/L, calcium chloride 0.50 g/L. The yield of carotenoid reached 8.20 mg/L, which was 95.90% higher than that of the original medium. Glu, VB1 and ethanol were selected as fermentation addictives, after Orthogonal Test, the carotenoid contents increased by 25.73% when adding 0.16 g/L Glu, VB1 10 mg/L and ethanol 0.1% (V/V). After the above optimization, the astaxanthin content increased 6.86 folds, which is 9.12 mg/L. The carotenoids content increased 2.44 folds, which is 10.31 mg/L. The biomass increased 2.27 folds, which is 11.35 g/L. Phaffia rhodozyma grows in the mild temperature range of 0 to 27 ℃, in this work, a thermotolerant mutant was selected through UV-irradiation. It can grows at 35 ℃, and showed increased carotenoid content. The optimal growth temperature for this mutant is 30 ℃. But the mutant can only produce carotenoids with little astaxanthin accumulation.

利用BP神经网络实现三维物体姿态的测定

本文利用BP(Back-Propagation)人工神经网络对三维物体的姿态测定进行了研究。姿态测定一直缺少通用而实际的方法,人工神经网络由于具有强大的自组织、自适应学习能力,迅速的并行信息处理能力,可望解决这个问题。但现有BP算法存在训练慢和易陷入局部最小两个问题.本文提出的级联形式网络结构,使BP网络的训练速度大为提高,陷入局部最小的可能性大为降低。利用这种级联结构对飞机模型姿态测定,取得了较好的实验结果。

基于神经网络的激光熔覆高度预测

激光成形过程中,对熔覆高度进行实时检测,从而实现熔覆高度闭环控制是成形高质量零件的保证。激光成形过程是一个多参数耦合的非线性过程,大量激光参数对成形熔覆表面质量具有重要影响。在分析激光参数对熔覆高度影响的基础上,建立利用激光工艺参数预测熔覆高度的误差反向传播(Backpropagation,BP)神经网络模型,完成了网络算法设计。通过激光成形试验采集样本,利用训练样本对所建立的网络进行训练,完成网络输入输出高度映射关系,并利用测试样本对所训练的网络进行检验。仿真试验表明,神经网络熔覆高度预测模型具有很高的精度,验证了该预测模型在理论和实践上的可行性与有效性。神经网络熔覆高度预测模型为实现激光加工过程熔覆高度实时预测与闭环控制打下基础,对提高成形产品质量具有重要意义。

Financial time series analysis : Chaos and neurodynamics approach

This work aims at combining the Chaos theory postulates and Artificial Neural Networks classification and predictive capability, in the field of financial time series prediction. Chaos theory, provides valuable qualitative and quantitative tools to decide on the predictability of a chaotic system. Quantitative measurements based on Chaos theory, are used, to decide a-priori whether a time series, or a portion of a time series is predictable, while Chaos theory based qualitative tools are used to provide further observations and analysis on the predictability, in cases where measurements provide negative answers. Phase space reconstruction is achieved by time delay embedding resulting in multiple embedded vectors. The cognitive approach suggested, is inspired by the capability of some chartists to predict the direction of an index by looking at the price time series. Thus, in this work, the calculation of the embedding dimension and the separation, in Takens‘ embedding theorem for phase space reconstruction, is not limited to False Nearest Neighbor, Differential Entropy or other specific method, rather, this work is interested in all embedding dimensions and separations that are regarded as different ways of looking at a time series by different chartists, based on their expectations. Prior to the prediction, the embedded vectors of the phase space are classified with Fuzzy-ART, then, for each class a back propagation Neural Network is trained to predict the last element of each vector, whereas all previous elements of a vector are used as features.

Desenvolvimento de um sistema dinâmico para predição de cargas elétricas por redes neurais através do paradigma de programação orientada a objeto sob a linguagem JAVA

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Evaluating the performance of a EuroDivisia index using artificial intelligence techniques

We compare two methods in order to predict inflation rates in Europe. One method uses a standard back propagation neural network and the other uses an evolutionary approach, where the network weights and the network architecture is evolved. Results indicate that back propagation produces superior results. However, the evolving network still produces reasonable results with the advantage that the experimental set-up is minimal. Also of interest is the fact that the Divisia measure of money is superior as a predictive tool over simple sum.