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In this research, we have developed a model for predicting the profitability class of a movie namely "Profit" and "Loss" based on the data about movies released between the years 2010 and 2015. Our methodology considers both historical data as well as data extracted from the social media. This data is normalized and then given a weight using standard normalization techniques. The...
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...
Attracting more students into science and engineering disciplines concerned many researchers for decades. Literature used traditional statistical methods and qualitative techniques to identify factors that affect student retention up most and predict their persistence. In this paper we developed two neural network models using a feed-forward backpropagation network to predict retention for students...
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...
In this paper, a novel two hidden layers artificial neural network (2HLANN) model is proposed to predict the dynamic nonlinear behavior of wideband RF power amplifiers (PAs). Starting with a generic low-pass equivalent circuit of the PA, several circuit transformations are applied in order to build an appropriate artificial neural network structure and improve the modeling accuracy. This approach...
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...
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...
This paper presented a new prediction model for material property (strength of materials for gray cast iron) based on composition and microstructure using a recent learning algorithm called Sensitivity Based Linear learning Method (SBLLM). This method was proposed in order to address the problems of back propagation learning algorithm for feed forward neural network. Thus we have made use of this...
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...
Gas filow-volume controlled by many factors, the trend is complex, the accurate mathematical model to predict, in view of this situation, the paper attempts to grey dynamic model based on artificial neural network, organic combination of intelligent analysis method, structural gray neural network combination forecast model, based on Visual Basic 6.0, meanwhile, corresponding calculation program is...
Short-term prediction of intelligent traffic flow is in favor of road unblocked and vehicle waiting strategy. The algorithm of short-term prediction of intelligent traffic flow based on back propagation(BP) neural network and autoregressive integrated moving average (ARIMA) model can solve it partially. Firstly, Establishing a BP neural network sub-model and ARIMA sub-model, Then taking BP neural...
The crude oil demand is growing rapidly in China, driven by its rapid industrialization and motorization. China has already become the second-largest oil importer nation in the world, after the United States. The dynamic GM(1,1) model of grey theory is used to develop the dynamic GM(M,N) model to forecast the crude oil consumption and production in China. In order to improve the forecasting accuracy,...
With financial globalization, the rapid development of financial derivatives and the complexity of banks management, operational risk measurement and management in commercial bank management is becoming increasingly important. How to effectively predict, control and prevent operational risk in commercial banks have become an important issue. Using BP neural network model to predict the risk has its...
Assessing typhoon losses quickly is the foundation to allocate and deliver relief supplies for helping disastrous people pull through difficulties during the process of typhoon and post-disaster immediately. Therefore, a new method for predicting typhoon losses based on back-propagation neural network is presented by using typhoon characters data, historic typhoon loss data, relative geographical...
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 contrast between...
In this experiment, by using the method of artificial neural network and DPS 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...
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