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The continuous decline of ground water level is one of the important factors that affect development of national economy and society. Based on the DE-BP (back propagation-differential evolution) neutral network, the predicting model of ground water level is presented. The precision of the model is checked using the monitoring data in Zhangjiakou area. The comparisons between the predicted results...
It is difficult to accurately analyze forecasting of tax income. This thesis establishes a tax forecasting model based on BP neural network to analyze impacts imposed on tax income by changes of the following economic factors: industrial added value, total investment in fixed assets, total volume of import and export, total volume of fiscal expenditure, resident consumption level, etc. The thesis...
This paper documents the results of the research involving neural network-based blood glucose level forecasting systems for insulin-dependent diabetes patients. Forecast is made for continuous subcutaneous insulin injections and continuous subcutaneous glucose measurements. Elman, layer-recurrent, and NARX network architectures were considered in the research. The influence of the network architecture,...
Bio-signal analysis is one of the most important approaches to biomedical engineering. The health information such as ECG, PCG, EMG and EEG are often recorded in digital format to be analyzed. In this paper, a bio-signal analyzing Web service system using Support Vector Machines (SVM) classifier technique is proposed. The bio-signals are recorded in digital format as the input of the system. In addition,...
The north of Jiaozhou Bay has become the important region for the development strategy of Qingdao in China, because the development space of the old city district gets saturated. The Moshui River will become the main contaminated river of this area. Neural network was used to build the water quality prediction model of the discharge outlet of the river to predict the concentration of COD, ammonia...
It is essential to estimate the status of the economy indices for the national welfare and people's livelihood. In this paper, we present a neural network method of forecasting economy indices based on the neural network theory. The feasibility of this method is discussed by means of its application to a simulant economy system.
The use of multistage evaporators, motivated by the energy economy from reusing the flashed steam is common in a wide range of process industries. Such evaporators however present several control problems which manifest in the form of strong interactions among the many process variables, significant dead times, tendency to open-loop instability and severe nonlinearities. In this paper, a nonlinear...
The price index of commodity retail is a relative number reflected the price of commodity retail variable trend and degree in a given period. The price of commodity retail's variation directly effect residents' payment and national financial revenue. So in order to forecast the variable trend of commodity price index, a new model is presented that based on support vector machine (SVM). Firstly, the...
Under the opening economic circumstances, forecasting the risks of capital flow has special significance. For effectively early warning the risks associated with capital flow, this study applies support vector machine (SVM) to the domain of capital flow in an attempt to suggest a new model with better explanatory power and stability. To serve this purpose, a grid-search technique using 5-fold cross-validation...
In this paper, we propose a vector input neural network model. The architecture of this network is composed by two parts: single vector immutiply and mix (de-mix) matrix process. The model can be described as a high dimension neural network operator. Simplify this model bring to a high dimension array as the kernel of the network. The high dimension neural network is usable in many fields especially...
Fungal growth leads to spoilage of food and animal feeds and to formation of mycotoxins and potentially allergenic spores. There is a growing interest in predictive modeling microbial growth as an alternative to time consuming traditional, microbiological enumeration techniques. Several statistical models have been accounted to describe the growth of different micro-organisms. However neural networks,...
In this paper, a novel artificial neural network ensemble rainfall forecasting model is proposed for rainfall forecasting based on K-nearest neighbor nonparametric estimation of regression. In this model, original data set are partitioned into some different training subsets via Bagging technology. Then different ANN algorithms and different network architecture generate diverse individual neural...
Credit scoring models are very important tools for credit granting institutions to assess the credit risk of their customers. Most previous researches focus on improving predictive accuracy of models. In this research, a weighted LS-SVM credit scoring model with Area under ROC curve maximization is proposed and optimized by direct search. The tests on two real-world datasets show that it is effective...
In order to solve the manufacturing time series forecasting problem, a grey support vector machines (GSVM) with differential evolution algorithms is proposed. GM (1, N) model of grey system is used to add a grey layer before neural input layer and white layer after support vector machine layer. Differential evolution (DE) algorithm is used to determine free parameters of support vector machines. Evaluation...
In this study, we develop some deterministic metamodels to quickly and precisely predict the future of a technically complex system. The underlying system is essentially a stochastic, discrete event simulation model of a big baggage handling system. The highly detailed simulation model of this is used for conducting some experiments and logging data which are then used for training artificial neural...
Rapid, accurate and reliable measurements of biological oxygen demand (BOD) are a key basis for monitoring and controlling wastewater treatment processes (WWTP). A kind of soft measurement based on the dynamic neural network (DNN) is proposed in this paper, which can be used to monitor and model the important parameters of the wastewater treatment process on-line. The main parts of the soft measurement...
Support vector machine is a new machine learning technique developed on the basis of statistical learning theory, which has become the hotspot of machine learning because of its excellent learning performance. Based on analyzing the theory of support vector machine for regression (SVR), a SVR model is established for predicting the output in fully mechanized mining face, and then realizes the model...
With the rapid development of real estate, the risk of investment is also increasing rapidly. So the risk of predicting and controlling the real estate investment has become the key to the success or failure of the project. In this paper, a support vector machine (SVM) modeling approach for real estate investment risk prediction is proposed at first, which is made use of its merits of structural risk...
Many researchers have already published huge number of papers comparing Autoregressive (AR) model, a model based on Box-Jenkins methodology, and Back Propagation Artificial Neural Network (BPANN) in financial time-series forecasting. Among them, some compared SVMs and BPs taking AR as a benchmark in forecasting the six major Asian stock markets. They showed that both the SVMs and BPs outperform the...
An actual physical simulation model was constructed to simulate the course of oil and water migration. Under certain physical property conditions, we simulated the water injection well and the oil well on the physical simulation model, and continuous measured online the oil and water content of different area of model in three-dimensional space using the 512 routes resistivity measuring circuit, then...
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