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Algorithms that encode images using a sparse set of basis functions have previously been shown to explain aspects of the physiology of a primary visual cortex (V1), and have been used for applications, such as image compression, restoration, and classification. Here, a sparse coding algorithm, that has previously been used to account for the response properties of orientation tuned cells in primary...
BP neural network (back propagation neural network) is a mathematical model for machine learning. It has a strong advantage in terms of prediction of the future events, and taking into account the different applications, its impact factors are different, which makes the model complex and diverse. A general modeling approach is proposed, which creates and stores BP neural network model dynamically,...
Base bleed propellant is an important component of the increasing rang projectile using base bleed technology. Unsteady strongly combustion leads to extinguish, reignition or critical state which produce an effect on rang dispersion. The burning behavior is determined by the initial pressure of combustion chamber and the maximum pressure decay rate, which was investigated by simulation experimental...
Through the application of genetic algorithms (genetic algorithm, simplified as GA) and BP(Back Propation) neural network, I built a prediction model of roses diseases, in which I choose six indicators as the input of network, they are the minimum temperature, maximum temperature, average temperature, minimum humidity, maximum humidity, average humidity in the greenhouse, then I choose three diseases...
Aiming at the disadvantages of prediction model of single BP neural network, a prediction model was presented by combining AdaBoost algorithm and BP neural network for improving the forecasting accuracy of single BP neural network. A new updating method is proposed for the characters of ensemble BP neural network based on AdaBoost. The new method can update the model effectively and overcome the disadvantage...
During soft rock roadway construction, the deformation of surrounding rocks is a significant factor in stability evaluation. However, the deformation still has long duration, obvious nonlinear effect after the soft rock roadway construction accomplishment. Certainly, this potential stability change will make the maintenance cost increased in the future. We propose a Time Series Prediction model based...
This paper discusses on the adaptive neural network model for predicting the energy consumption at a metering station. The function of the metering system is to calculate the energy consumption of the outgoing gas flow. To ensure the robustness of the developed model, it is suggested to make the model an adaptive model that will periodically update the weights. This will ensure the reliability of...
Artificial Neural networks ANNs are dynamic systems which have the ability not only to capture the relationship between input and output parameters of complex systems but also highly effective when there is no any mathematical formula or model for the system. Therefore, they are very potential and appropriate for design of systems whose functions cannot be expressed explicitly in the form of mathematical...
A method for prediction discrete data is presented in this article. In order to forecast the discrete data, the experiment that use the GM (1,1) and BP networks to predict discrete data are respectively executed, we found that AGO operation in the GM method can effectively reduce randomness of the discrete data, so AGO operation is applied into the BP network method. According to the result of the...
Inflation is one of the most important macroeconomic variables. However, the behavior of inflation is so complicated that both economists and statisticians have strived to model and forecast inflation for years. In this study, the linear AS-AD model and nonlinear artificial neural network (ANN) technique are both employed to have a better understanding of the inflation behavior in China from 1992...
Gross Domestic Product (GDP) is a benchmark for economic production conditions of a country. Estimates of economic growth in the coming year in a country has important roles, among others as a benchmark in determining business plans for business entities, and the basis for devising government fiscal policy. Artificial Neural Network (ANN) has been increasingly recognized as a good forecasting tool...
Based on the data of household income of Shanghai low-rent housing families, a GM(1,1) forecast model and a Back-Propagation Artificial Neural Network (BPANN) forecast model are established respectively to predict the average household income of low-rent housing families. The comparison between the GM(1,1) and the BPANN model showed that the BPANN model is better than the GM(1,1) model at the aspects...
The thesis introduces grey system model and BP neural network. Through making full use of the merits of GM(1.1) and neural network model and overcoming their drawbacks, we construct the grey residue amending combined and prediction model based on BP Neural network, and such combined model as "combined prediction model= tendency prediction model/GM(1.1)+neural network model", and makes a...
To make an accurate prediction about the amount of equipment maintenance materials consumption (EMMC), which plays an important role of equipment maintenance materials support, precondition and management, an LM algorithm prediction model of EMMC established based on the improved BP neural network algorithm by means of history data processing, and which has been discussed and verified through example...
Nonparametric Linear Regression and Artificial Neural Network models have been developed based on different perspectives and assumptions. In this paper a survey is made to compare the predictive performances of the nonparametric models of closing prices of Stock Index data, where the data is non normal. Comparative studies with the existing statistical prediction models indicate that the proposed...
Determining of the torpedo's service year reasonably, it is an effective way to reduce the military expenses expenditure, and forecast the torpedo economic life. We can forecast the data of exponential use maintenance cost by using the grey metabolism GM(1,1) model. In order to improve the prediction precision, the data was divided into several groups, and prediction residual was modified by using...
The paper studies the application of principal component analysis and ANN (Artificial Neural Networks) for pre-warning of enterprise financial crisis, analyzes the factors of financial crisis, and constructs the model of the enterprise financial crisis with principal component analysis and ANN. It integrates simplifying of enterprise financial crisis index, dynamic learning of financial crisis knowledge...
This thesis introduces the forecasting methods of domestic and foreign road traffic flow, analyzes the advantages and shortcomings of all sorts of traffic flow forecasting methods and the actual forecasting effects. For the complexity of the urban traffic, the precision of some current traffic flow forecasting methods is not high. With respect to these questions, this thesis applies the chaotic neural...
This work gives a comparative study of two modeling methods to predict the fiber diameter of melt blowing nonwovens from the processing parameters (polymer flow rate, polymer melt temperature, initial air velocity and die-to-collector distance). After measuring fiber diameter, evaluations of data were performed by using ANN model and mathematical-physical model. On the basis of the results obtained,...
Cement rotary kiln calcining process is a kind of functional equipment for fuel combustion, heat exchange, and chemical reaction. A complex succession of chemical reactions takes place as the temperature rises. One can not establish a precise mathematical model of rotary kiln, so it is difficult to achieve its optimal control. In order to accurately reflect the system dynamic characteristics, we use...
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