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In the process of evaluation logistics company performance by BP neural network, the more difficult problem is to determine the evaluating sample expectations, while the C2R DEA cross-evaluation model is able to identify the rank and grade of evaluating samples (the unit of making decision). In this paper, we set the cross-model evaluation results to the sample expectations, integrate BP neural network...
Economic security has been a serious problem which threatens and hampers the sustainable growth of the economy. The paper sets up appraising indicator system of economic security on the basis of PSR model, and researches economic security for 21 coal cities by using BP artificial neural net. As innovation of researching economic security, BP artificial neural net has the better ability in level identification,...
A water quantities allocation arithmetic was proposed, Radial basis function neural network (RBFNN) was designed, and simulated annealing arithmetic was adopted to adjust the network weights. MATLAB program was compiled; experiments on related data have been done employing the program. All experiments have shown that the arithmetic can efficiently approach the surface with 10-4 mm error precision,...
Construction of intelligent transportation system is a necessary requirement for the development of transport, and LPR(License Plate Recognition) is an important part of construction of intelligent transportation system, Therefore, the research of license plate recognition method is of importance. The license plate character is recognized by building BP artificial neural network in this paper, it...
Computer-supported collaborative learning (CSCL) is one of the more dynamic research directions in educational psychology. In order to investigate successful collaboration with different factors affecting the measurement, Back propagation artificial neural network (BP-ANN) modeling is applied which can deal with process measurement and real-time analysis, even though, is highly complex and challenging,...
With the moving dune in sandy land of Northwest Liaoning province as the research object, its water variation in soil was simulated and studied based on a BP Neural Network model. With principal meteorologic factors that affect soil water, such as precipitation and evaporation, as the input variables and the water content in soil as the output variable, a soil-water prediction model based on BP NN...
Personal Credit Scoring is of great significance for commercial banks to circumvent credit consumption, the original BP algorithm's convergence rate is slow, learning precision is low, the training process is easy to fall into local minimum, this paper presents an improved algorithm with variable learning rate based on BP algorithm, and applied to simulate personal credit scoring. After comparing...
On the basis of analyzing the significance of assessing operation capability in SMB, the Appraisal-index system of operation capability for SMB is built, and appraisal model is established using BP neural network. The conjunction weights of the neural network are continuously modified layer by layer from output layer to input layer in the process of neural network training to reduce the errors between...
This article established artificial neural networks based on improved ant colony optimization evaluation model for residential performance. Firstly, on the basis of comprehensive analysis of the effects factors of residential building's performance, considering of the advantages of dealing with non-linear object of neural network, the neural network is trained by the sample data. While training neural...
The last decade witnessed a significant increase in net private capital inflows in China. Some of them are short-term capital flows, which are typically considered to be highly volatile. For effectively forecasting the short-term capital flows, a three-layered neural feedforward network was employed in this paper. In light of the weakness of the conventional Back-Propagation algorithm, the Levenberg-Marquardt...
In order to forecast industrial-waste-emissions much more accurate, a hybrid system to improve the precision of forecasting, which is the BP neural network. At first, we cluster the data of the export products and industrial-waste-emissions in China. And then, the data is used to develop classification rules and trains BP neural network. It was also proved that the model was feasible and easy to use...
In the paper, the fuzzy neural network method is introduced into the field of enterprise performance evaluation in order to overcome the deficiencies of the traditional methods. We reference the state-owned capital performance evaluation index system, issued by The Ministry of Finance and other six ministries and commissions, to build evaluation index of this paper, propose and employ a hierarchical...
An intelligent method on short-term prediction on water bloom of BP neural network based on rough set and wavelet analysis is proposed in this paper. This method analyzes factors of effecting the outbreak of water bloom, and these many factors which were processed by reduction method based on rough set were used as input information of the prediction model; after analyzing the main input information...
In order to forecast industrial-waste-emissions much more accurate, we choose BP neural network and multiple linear regression to improve the precision of forecasting. The results of this comparison is that the BP neural network is distinctly superior. It was also proved that the model was feasible and easy to use, and had high accuracy.
Traditional method about forecast of energy demand, Trend Extrapolation, can't study the information supplied with date effectively, and BP neural network has the great power of goal learning, which can dig potential function in the date. The article design the GDP and other factors as input variables, and use steepest descent back propagation to adjust the weight and threshold of network. We choose...
This paper proposes an effective hybridization of grey relational analysis (GRA) and Backpropagation Particle Swarm Optimization (BP_PSO) for time series forecasting. The hybridization employs the complementary strength of these two appealing techniques. Additionally the combination of GRA and BP as cooperative feature selection (CFS) has successfully assessed the importance of each input variable...
The main purpose of this paper is to establish a monthly water quality predicting model of Feitsui Reservoir in northern Taiwan. This model is based on data from nutrient loads to simulate the dynamic nutrient concentration in reservoir. The proposed model employed artificial neural networks (ANNs) with the back-propagation algorithm which can obtain a highly nonlinear relationship to predict the...
The simulation of pedestrian has been studied for a long time from various points; however, most of them have not considered the psychological and terrain factors for pedestrians. Firstly, we develop the fundamental model of pedestrian simulation based on Cellular Automata. To consider the terrain factors, this paper embeds neural network into these agents to ensure intelligent and realistic. The...
The case selecting of tax check in real estate industry has been the subject of considerable research effort in recent years. In this paper, we propose Wavelet neural networks (WNN) for it, which used in many fields because of their advantages over neural network and wavelet analysis as they achieve faster convergence and discriminance more precise. A suit of evaluation indices are constructed as...
The focus of this paper is placed on evaluation methods of the flood disaster grade: the attribute recognition (AR) and the BP neural network. In the first method the entropy value theory is applied to establish an entropy-based AR model; the second method adopt Levenberg-Marquardt (LM) algorithm to achieve a higher speed and a lower error rate to overcome the shortcomings of the traditional BP algorithm...
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