The Neural Network Analysis Method Applied in Prediction of Coal-Bed Methane Content

Sensitivity study, the premise of the analysis of reservoir damage mechanism, is significant to optimize all the links of drilling and development process, as well as to develop the systematic oil and gas reservoir protection technology scheme. To study a variety of methods developed in recent years for the reservoir sensitivity prediction, it is found that using the combination of single correlation analysis and multiple regression analysis is a new, ideal, and rapid method for the prediction of reservoir sensitivity. Extract the relevant information of sensitivity on the basis of conventional core analysis and sensitivity analysis of minerals, and apply the single correlation analysis and multiple regression analysis to predict the reservoir sensitivity, the accuracy rate can reach 85%, which basically meets the needs of reservoir sensitivity prediction. Compared with the single prediction method, the prediction results obtaining from the combination of the two methods are obviously improved. It is simple, highly applicable, and has a clear physical meaning. The most common method of reservoir sensitivity prediction is sensitivity experiment. Conventional reservoir sensitivity experiment method requires a large number of cores, and the test is a long term procedure, while, during the process of drilling and cutting cores as well as pretreatment for the experiment, different degrees of damage may have been caused to cores. Therefore, conventional evaluation method needs plenty of manpower and material resources, it cannot make sure that the predicting outcome of each time is exactly correct however. Various methods for the fast prediction of reservoir sensitivity have been developed in recent years, such as Elman neural network, BP neural network, grey theory appraising method, multiple discriminate analysis, fuzzy mathematical method and so on, while, there exists defect in each method. Elman network can be regarded as forward neural network which possesses partial memory unit and feedback connection. Its major structure is feedback connection, which is used to memorize the output value of the previous moment, and its convergence speed is slow due to the fixed connection weight. It usually adopts BP neural network model in reservoir sensitivity prediction. However, there exists disadvantages like low convergence speed and failure in global optimization in the conventional BT algorithm towards BP algorithm. Neural network method changes the characteristics of all problems into figures, inference into numerical calculation, while neural network fails to carry out work when the data is insufficient. Besides, it requires corresponding complicated computer program when taking use of neural network method, thus the widespread use of this method has been limited. Grey theory is a new approach aims at studying the uncertainty of less data and poor information, which owns the advantage of being required with less data and small amount of calculation, thus it is extensively used in some certain industries. Within the grey model, the plane nipped between the upper and lower bounds of the forecasted future value is called gray plane, which is unfold in a shape of trumpet, that is to say, the farther in the future time, the larger grey interval of prediction value, in other words, the bigger the grey scale, the smaller the practical significance of the prediction value. Multiple discriminate analysis first divides sensitivity and the known samples of fabric parameter into several groups based on the size of sensitivity, respectively establishing the discriminant functional equation of each group of sample. It is impossible to grouping in detail, thus the predicting outcome is only an interval, but not a specific numerical value. 2nd Workshop on Advanced Research and Technology in Industry Applications (WARTIA 2016) © 2016. The authors Published by Atlantis Press 556 Based on the current method of fast prediction for reservoir sensitivity, this article combines with the previous research results, synthesizes the applied single correlation analysis and multiple regressions analysis to forecast reservoir sensitivity, getting over the disadvantages of other methods in forecasting reservoir sensitivity, supplementing the existed defects. 1. SINGLE CORRECTION ANALYSIS When one or several interrelated variables are taken certain values, although the precise value of its another corresponding variable cannot be confirmed, it still changes within a certain rage in accordance with certain rules. The dependency relation objectively exists among undemanding and uncertain quantities of such phenomenon is known as correlativity. Back in 1980, British statistician Pearson had put forward a calculation formula of measuring the linear relationship between two variables, see formula (1), r usually acts as product moment correlation coefficient. The value range of correlation coefficient r is from -1 to 1; the size of |r| reveals the strong and weak point of the linear relationship between variable x and y. The correlation coefficient value significance and correlative degree evaluation standard see table 1 and table 2.

Use of neural networks to achieve dynamic task allocation: a flexible manufacturing system example

The dynamic load in the soil directly leads to the damage of underground structures upon explosions. In this study, a method to predict blast load on underground structure surface based on the neural network was developed to study the load distribution under close-in detonation. First, taking the underground utility tunnel as the experimental structure, 52 groups of field blast tests were conducted on the surface load mechanism, and the surface load data samples were obtained. Second, the key influencing parameters of the reflected blast load were obtained through the dimensional analysis method, and the backpropagation neural network model was constructed based on the test data using the Levenberg–Marquardt algorithm to train and optimize the neural network. Finally, the accuracy of load prediction results was compared and evaluated among the neural network, empirical formula, and nonlinear regression analysis (NRA) methods. It is found that the input parameters of combined variables can further improve the prediction accuracy of the neural network compared with the input parameters of single physical variables. Compared with the empirical formula method and the NRA method, the neural network model with input parameters of combined variables provided the most accurate prediction. The load distribution under typical conditions calculated by the neural network showed that the explosive setting parameters impact the uneven shape of blast load on the structure surface. The increase in explosive equivalent and depth reduces the nonuniformity of load distribution, while the decrease in explosion distance increases the nonuniformity of load distribution.

YAHSGS LLC and Oak Ridge National Laboratory (ORNL) established a CRADA in an attempt to develop a predictive system using a pre-existing ORNL computational neural network and wavelets format. This was in the interest of addressing national needs for toxicity prediction system to help overcome the significant drain of resources (money and time) being directed toward developing chemical agents for commerce. The research project has been supported through an STTR mechanism and funded by the National Institute of Environmental Health Sciences beginning Phase I in 2004 (CRADA No. ORNL-04-0688) and extending Phase II through 2007 (ORNL NFE-06-00020). To attempt the research objectives and aims outlined under this CRADA, state-of-the-art computational neural network and wavelet methods were used in an effort to design a predictive toxicity system that used two independent areas on which to base the system’s predictions. These two areas were quantitative structure-activity relationships and gene-expression data obtained from microarrays. A third area, using the new Massively Parallel Signature Sequencing (MPSS) technology to assess gene expression, also was attempted but had to be dropped because the company holding the rights to this promising MPSS technology went out of business. A research-scale predictive toxicity database system called Multi-Intelligent System for Toxicogenomic Applications (MISTA) was developed and its feasibility for use as a predictor of toxicological activity was tested. The fundamental focus of the CRADA was an attempt and effort to operate the MISTA database using the ORNL neural network. This effort indicated the potential that such a fully developed system might be used to assist in predicting such biological endpoints as hepatotoxcity and neurotoxicity. The MISTA/LiverTox approach if eventually fully developed might also be useful for automatic processing of microarray data to predict modes of action. A technical paper describing the methods and technology used in the CRADA has been published. This paper was entitled “A Toxicity Evaluation and Predictive System Based on Neural Networks and Wavelets” and appeared in an American Chemical Society peer-reviewed publication this year (J. Chem. Inf. Model. 47: 676685, 2007). A patent application was filed but later abandoned.

Comparison of the Kriging and neural network methods for modeling foF2 maps over North China region

این مطالعه به منظور بررسی پیش بینی شاخص های عملکرد تولیدی در مرغان تخم گذار با استفاده از شبکه های عصبی مصنوعی و رگرسیون غیرخطی چندگانه انجام شد. بررسی بر روی اطلاعات چهار دوره متوالی پرورش در یک واحد پرورش مرغ تخم گذار صورت گرفت. روش های داده-کاوی شامل رگرسیون خطی و غیر خطی، شبکه عصبی پرسپترون سه لایه، شبکه عصبی پرسپترون چهار لایه و شبکه عصبی با تابع پایه ای شعاعی بود. در این مدل ها از متغیرهای سن گله، میزان خوراک مصرفی و فصل تولید به عنوان متغیر پیشگو و شاخص های عملکرد تولیدی شامل درصد تخم-گذاری، وزن توده ای تخم مرغ تولیدی و ضریب تبدیل غذایی به عنوان متغیر پاسخ استفاده شد. نتایج نهایی رگرسیون های خطی نشان داد که برای تمامی متغیرهای وابسته مورد مطالعه متغیر مستقل سن گله معنی دار می باشد. بنابراین رگرسیون غیر خطی شاخص های عملکرد تولیدی در مقابل سن برای مقایسه با شبکه های عصبی مختلف مورد بررسی قرار گرفت و برای مقایسه کلیه مدل ها از ضریب تعیین (R2) و میانگین قدر مطلق خطا (MAE) استفاده شد. نتایج نشان داد بین شبکه های عصبی مصنوعی مختلف مورد مطالعه، شبکه با تابع پایه ای شعاعی بهتر از سایر مدل های در پیش‌بینی شاخص های عملکرد تولیدی مرغان تخم گذار عمل می کند.

This article has been retracted by the publisher.

In this paper we use the method of MRF and neural network to solve the problem of parameters estimation in non-rigid 3D movement. Firstly, the method of MRF is used for modeling the local motion correlation of each feature point, and the 3D coordinates of each feature point are obtained. Then the method of neural network is used for clustering the feature points according to their motion situation. When the neural network reaches stabilization, we can get the motion parameters of each feature point. Finally, we correct the neighborhoods of each feature point according to motion parameters. The experimental results show that our algorithm can correctly estimate the non-rigid motion parameters.

Concerning the prediction problems’accuracy of the state-of-charge(SOC) of the battery，this paper proposes a prediction method based on an improved genetic algorithm-radial basis function neural network for power battery charged state. The prediction method, based on intensity of information interaction and neural activity, adjusts the size of the neural network online and solves the problem that radial basis function neural network structure adjustment influences the accuracy of charged state prediction. The simulation results show that，compared with the method of radial basis function neural network based on genetic algorithm , the accuracy of charged state prediction is more stable and more precise.

For the congestion phenomena of networks, it has been provided with a new prediction method for service flow (based on Particle Swarm Optimisation and Wavelet Neural Network Prediction PSOWNNP). Firstly, this method is using the wavelet exchange to resolve the service flow, and using its wavelet coefficient and metric coefficient as the sample data. Secondly, training the sample data is using the neural network method of the particle swarm optimisation in which it is applying the wavelet model for construction, and the prediction data for service flow will be obtained from this. At the same time, the prediction methods of wavelet neural network and BP neural network for particle swarm optimisation are analysed and compared through the simulation experiment, and the result for indicating the performance of AWNNP method is relatively good, with a tolerance of 17.21%.

This study focuses on predicting which firms will increase capital by issuing new stocks in the near future. Many stakeholders, including banks, credit rating agencies and investors, performs a variety of analyses for firms' growth, profitability, stability, activity, productivity, etc., and regularly report the firms' financial analysis indices. In the paper, we develop predictive models for rights issues using these financial analysis indices and data mining techniques. This study approaches to building the predictive models from the perspective of two different analyses. The first is the analysis period. We divide the analysis period into before and after the IMF financial crisis, and examine whether there is the difference between the two periods. The second is the prediction time. In order to predict when firms increase capital by issuing new stocks, the prediction time is categorized as one year, two years and three years later. Therefore Total six prediction models are developed and analyzed. In this paper, we employ the decision tree technique to build the prediction models for rights issues. The decision tree is the most widely used prediction method which builds decision trees to label or categorize cases into a set of known classes. In contrast to neural networks, logistic regression and SVM, decision tree techniques are well suited for high-dimensional applications and have strong explanation capabilities. There are well-known decision tree induction algorithms such as CHAID, CART, QUEST, C5.0, etc. Among them, we use C5.0 algorithm which is the most recently developed algorithm and yields performance better than other algorithms. We obtained data for the rights issue and financial analysis from TS2000 of Korea Listed Companies Association. A record of financial analysis data is consisted of 89 variables which include 9 growth indices, 30 profitability indices, 23 stability indices, 6 activity indices and 8 productivity indices. For the model building and test, we used 10,925 financial analysis data of total 658 listed firms. PASW Modeler 13 was used to build C5.0 decision trees for the six prediction models. Total 84 variables among financial analysis data are selected as the input variables of each model, and the rights issue status (issued or not issued) is defined as the output variable. To develop prediction models using C5.0 node (Node Options: Output type = Rule set, Use boosting = false, Cross-validate = false, Mode = Simple, Favor = Generality), we used 60% of data for model building and 40% of data for model test. The results of experimental analysis show that the prediction accuracies of data after the IMF financial crisis (59.04% to 60.43%) are about 10 percent higher than ones before IMF financial crisis (68.78% to 71.41%). These results indicate that since the IMF financial crisis, the reliability of financial analysis indices has increased and the firm intention of rights issue has been more obvious. The experiment results also show that the stability-related indices have a major impact on conducting rights issue in the case of short-term prediction. On the other hand, the long-term prediction of conducting rights issue is affected by financial analysis indices on profitability, stability, activity and productivity. All the prediction models include the industry code as one of significant variables. This means that companies in different types of industries show their different types of patterns for rights issue. We conclude that it is desirable for stakeholders to take into account stability-related indices and more various financial analysis indices for short-term prediction and long-term prediction, respectively. The current study has several limitations. First, we need to compare the differences in accuracy by using different data mining techniques such as neural networks, logistic regression and SVM. Second, we are required to develop and to evaluate new prediction models including variables which research in the theory of capital structure has mentioned about the relevance to rights issue.

The article presents the issue of monitoring the sociopsychological state of society in the period of epidemics by means of neural network algorithms and methods. Publications of sociologists, psychologists, and philologists, who have created a number of methods for in-depth analysis of emotions and tonality of texts in the Internet media, including cognitive and interpretive decoding, are devoted to substantiating approaches and methods for studying the content of Internet content. The purpose of the study is to substantiate the methods and computer tools for studying the socio-psychological state of society in crisis situations, in particular epidemics, based on neural network technologies based on Internet resources. The article considers methodological approaches and particular methods of their computer implementation. It is shown that for the computer analysis of the psychological state of society in the context of the epidemic, it is necessary to adapt the methodology for designing neural network technologies, as well as systems for collecting and textual analysis of the content of electronic and Internet resources. An effective approach to creating such systems is embedding, which uses a dense vector representation of tokens in a multidimensional space, the dimension of which should be selected experimentally in the process of training and testing the developed artificial neural networks (ANN). For contextual neural network analysis, a multiclass-oriented ANN with regularization layers of the form “SpatialDropout1D”can be used. The neural network architecture can be built on fully connected layers with an activation function of the “ReLU” type. The scientific and applied significance of the results of neural network analysis based on Internet resources is the possibility of obtaining classified assessments and segmentation of target information about the psychological state of society during periods of epidemics. This information can be used to effectively counter information threats to society.

Water consumption of plants is a key parameter for formulating irrigation system, and the precise prediction play a important role in improving the use efficiency of limited water resources. In this experiment, by using the method of artificial neural network and MATLAB DATA PROCESSING SYSTEM combined with the meteorological data of air temperature, relative air humidity, solar radiation, wind speed, soil water content and dew point temperature as the input variable, the author established the artificial neural network system to forecast the seedling water consumption of P.×euramericana cv."74/76", and through the experiments it has been examined that two neural network system models can be applied in forecasting water consumption of seedlings, and the average relative error of Back Propagation (BP) neural network prediction model was 0.07, the General Regression Neural Network (GRNN) prediction model was 0.05, moreover, the latter had good stability, while that of the former was poor. Therefore, we propose that GRNN model can be used in prediction of seedling water consumption. Furthermore, the maximum relative error of GRNN predication model was 0.106, the minimum relative error was 0.015. The GRNN model is superior to the BP neural network model that the former performs a higher forecasting accuracy with relatively shorter time consumption and faster speed in training.

The Sixth International Symposium on Neural Networks (ISNN 2009)

BackgroundCirculating tumor cells (CTCs) are the critical initiators of distant metastasis formation. In which, the reciprocal interplay among different metastatic pathways and their metastasis driver genes which promote survival of CTCs is not well introduced using network approaches.MethodsHere, to investigate the unknown pathways of single/cluster CTCs, the co-expression network was reconstructed, using WGCNA (Weighted Correlation Network Analysis) method. Having used the hierarchical clustering, we detected the Immune-response and EMT subnetworks. The metastatic potential of genes was assessed and validated through the support vector machine (SVM), neural network, and decision tree methods on two external datasets. To identify the active signaling pathways in CTCs, we reconstructed a casual network. The Log-Rank test and Kaplan–Meier curve were applied to detect prognostic gene signatures for distant metastasis-free survival (DMFS). Finally, a predictive model was developed for metastasis risk of patients using VIF-stepwise feature selection.ResultsOur results showed the crosstalk among EMT, the immune system, menstrual cycles, and the stemness pathway in CTCs. In which, fluctuation of menstrual cycles is a new detected pathway in breast cancer CTCs. The reciprocal association between immune responses and EMT was identified in CTCs. The SVM model indicated a high metastatic potential of EMT subnetwork (accuracy, sensitivity, and specificity scores were 87%). The DMFS model was identified to predict patients’ metastasis risks. (c-index = 0.7). Finally, novel metastatic biomarkers of KRT18 and KRT19 were detected in breast cancer CTCs.ConclusionsIn conclusion, the reciprocal interplay among critical unknown pathways in CTCs manifests both their survival in blood and metastatic potentials. Such findings may help to develop more precise predictive metastatic-risk models or detect pivotal metastatic biomarkers.