Improved BP Neural Network Model Research Articles

Database design of regional music characteristic culture resources based on improved neural network in data mining

With the improvement of living standards, Music Appreciation Art has gradually become the pursuit of people. As an important part of music resources, regional music is an indispensable treasure of music appreciation art. Regional music culture with its unique charm is constantly affecting modern people’s music appreciation ability. Fully learning regional music culture is the key to carry forward traditional culture. However, as an important part of the cultural treasure house, regional music characteristic culture resources lack reasonable digital storage. Therefore, reasonable and sufficient mining of regional music characteristic cultural resource data is of great significance to the protection of regional characteristic culture. In this paper, the database of regional culture and music characteristic resources is establishedby data mining technology. At the same time, combined with the improved BP neural network model to classify the regional characteristic music andcultural resources data. A set of database including classification, search, audition and storage is established in order to protect and spread the regional music characteristic cultural resources. Finally, it provides new ideas for cultural heritage and cultural heritage.

The variation differences of cultivated land ecological security between flatland and mountainous areas based on LUCC.

The purpose of this study was to explore the correlations between land use/cover change and cultivated land ecological security in flatland and mountainous areas. Firstly, the spatiotemporal variation characteristics of land use/cover change are described in conjunction with ArcGIS10.5 software based on remote sensing images of 2005 and 2015. Then, by establishing a pressure-support framework as an assessment indicator system and developing an improved BP neural network model via a genetic algorithm with the help of MATLAB2016a, the spatiotemporal dynamic changes of cultivated land ecological security in Yuxi City from 2005 to 2015 are evaluated. The results showed that the transformation of farmland area accounted for a large proportion of increased constructive land and land use/cover spatial variations were significantly different among counties, which manifested the changes in farmland and the construction land in flatland areas but also facilitated a mutual transformation of forest and grass in mountainous areas. Moreover, ecological security status presented a clear difference among counties due to their different land use/cover changes. The ecological security state of the flatland expressed a higher ecological pressure and lower ecological support, so the security grade was IV. Otherwise, the ecological security was superior and the security grade was level II or I in the mountainous areas. Thus, protection strategies for ecological security should be differentiated in the flatland areas and mountainous areas due to their different ecological security status brought by land use/cover change.

Pricing 50ETF Option Based on Genetic Algorithm BP Model

Pricing financial derivatives is focus in finance theory and practice. Comparing to the traditional parameter model pricing method, the neural network method has obvious advantages in solving this problem. In this paper,we will price the option of Shanghai 50ETF based on the improved BP neural network model (GABP). The results show that the effect of neural network is better than that of B-S model, and the accuracy of GABP model is higher than that of BP neural network model and B-S model.

Application of Improved Neural Network Algorithm in Image Denoising and Edge Detection

The knowledge involved in digital image processing is very wide, and there are many kinds of methods. Traditional image processing technology is mainly focused on the acquisition, transformation, enhancement, restoration, compression encoding, segmentation, edge extraction and so on. With the emergence of new tools and new methods, the image processing technology has been updated and developed. In this paper, an effective method for edge detection and image de-noising is proposed. In this article, the impulse noise detector is composed of a BP neural network (BPNN) and a decision switch. BPNN requires four input values, which are the current pixel value, grey median value, energy value, and contrast. To take these four values as the input values, the impulse noise detector can show good performance. The output of the BPNN is transferred to the decision switch, and the output value is converted to 0 or 1, which is used to distinguish whether the pixels are polluted. At this point, we introduce an additional impulse term and establish the improved BPNN model. The additional impulse term can effectively speed up the convergence of the network, avoid the emergence of the local minimum problem, and ensure the stability of the training process. In this way, the IBPNN filter of this paper only uses the information of the non polluted pixels to filter the noise pixels, which avoids the secondary pollution, and obtains a better performance. This algorithm has high PSNR value and strong detail information and edge preserving ability. Finally, the improved BPNN algorithm is applied to the image edge detection, and we use the improved neural network model to detect the edge of the image. Because the method can be used to include the prior knowledge, the IBPNN method is better than the traditional method in image edge detection.

Study on Regional Scientific and Technological Innovation Platform Innovations Evaluation Based on BP Neural Network

Based on the needs of the Regional Scientific and Technological Innovation Platform, innovations evaluation system was established by AHP. In order to simulate the experts' experiences and thinking, we used the improved BP neural network model. After training by putting in actual data, the improved BP neural network model was put in use to evaluate and manage the innovations created by the Regional Scientific and Technological Innovation Platform. With the accelerated process of economic globalization, the world economy appears regional characteristics obviously. In the rapid development of science and technology, the idea that innovation is a key factor in promoting economic growth has become the consensus by academia, enterprises and the government. Faced with intense competition in international technology and economy, most of developed countries have built first- class scientific and technological innovation platform as a support for innovative activity preferences and achieve leapfrog development of strategic initiatives. Regional scientific and technological innovation platform is established in the common needs of pivotal technology innovation in the relative industrial clusters, hunting, gathering and integration knowledge, information, technology, and other related resources for innovation. And provide each innovation subject in industrial clusters with public services and industrial technology innovation support by improving the liquidity, diffusibility of innovations in the platform. In a conclusion, regional scientific and technological innovation platform is an aggregate which linking the various nodes of innovation innovative needs and services effectively (1).Technological innovation platform, as an important carrier of innovation activities in the whole economic society, plays an important role in the regional science and technology and economic development. Thus in the present international science and technology and economic environment, a scientific theory and methodology is necessary to evaluate innovations which was created by enterprises in the innovation platform. It can not only test the efficiency of innovation in the platform, but also provide enterprises innovative resources in the future more efficiently. Nowadays, two important issues must be faced in the evaluation in the platform. First, how the quantitative results can be obtained as quickly as possible in the evaluation on a large number of innovations created by enterprises in the platform continually. Second, each evaluation result of innovations must be scientific, reasonable and consistent with the thinking of experts. These two issues are difficult to simultaneously solve by the general evaluation methods and expert evaluation group.

Breakout Prediction Based on BP Neural Network in Continuous Casting Process

An improved BP neural network model was presented by modifying the learning algorithm of the traditional BP neural network, based on the Levenberg-Marquardt algorithm, and was applied to the breakout prediction system in the continuous casting process. The results showed that the accuracy rate of the model for the temperature pattern of sticking breakout was 96.43%, and the quote rate was 100%, that verified the feasibility of the model.

Application of an Improved BP Neural Network Model in Enterprise Network Security Forecasting

Application of an Improved BP Neural Network Model in Enterprise Network Security Forecasting

Improved BP Neural Network Model Applied in Settlement Analysis

According to the settlement and deformation of pavement in subway construction, the paper improved on the basis of gray system theory and BP neural network forecasting model. The method is adapted to modified residual prediction model to predict with engineering examples. It provides decision-making basis for construction engineering and monitoring.

Regional GDP Prediction Based on Improved BP Neural Network Model

In this paper, an improved BP neural network model is proposed. In the model, the momentum factor can improve the training speed and avoid falling into local minimum. Steepness factor and adaptive learning rate can improve the convergence speed. The genetic algorithm is used to solve the problem of low training speed, low accuracy of prediction and easy to fall into local minimum of BP neural network. Then the improved BP neural network model is established to predict GDP of Anhui province. The result shows that it is better than the other models which are presented in this paper on forecasting GDP of Anhui province.

Study on Deformation Property of Soft Soil Based on Neural Networks

Based on the triaxial test results of soft soils, an error back propagation network predicting model for deformation property of soft soil is built. Improved BP neural network model is trained by additional momentum term, adaptive learning rate and Bayesian regularization performance function. Research shows that improved BP neural network model applied to predict soft soil foundation settlement, has fast computation, high accuracy, strong generalization ability, and good capability of matching the real data and the measured one. According to test data, the creep models can avoid any artificial assumption of complex constitutive equation, and can reflect nonlinear creep properties of soft soil objectively, thus has better fault-tolerance and more convenient than the traditional method.

Research on Network Traffic Identification Based on Improved BP Neural Network

Traffic identification is a key task for any Internet Service Providers (ISP) or network administrators. Neural network is an important research method on traffic classification, this paper introduces the important methods of traffic classification,through study on Principal Component Analysis(PCA) and BP neural network. An improved BP neural network to identify traffic is proposed and M OORE SET is used as dataset, meanwhile, building N OC SET dataset based on CERNET(China Education and Research Network).the experiment results show that the accuracy rate of traffic classification based on the improved BP neural network model is relatively high.Finally,this paper analyzes packet sampling impact on traffic identification.

An Inventory Controlled Supply Chain Model Based on Improved BP Neural Network

Inventory control is a key factor for reducing supply chain cost and increasing customer satisfaction. However, prediction of inventory level is a challenging task for managers. As one of the widely used techniques for inventory control, standard BP neural network has such problems as low convergence rate and poor prediction accuracy. Aiming at these problems, a new fast convergent BP neural network model for predicting inventory level is developed in this paper. By adding an error offset, this paper deduces the new chain propagation rule and the new weight formula. This paper also applies the improved BP neural network model to predict the inventory level of an automotive parts company. The results show that the improved algorithm not only significantly exceeds the standard algorithm but also outperforms some other improved BP algorithms both on convergence rate and prediction accuracy.

Highway Construction Cost Estimation Based on Improved BP Neural Network Model

Highway Construction Cost Estimation Based on Improved BP Neural Network Model

Flood Disaster Evaluation Based on Improved BP Neural Network Model

Floods have become increasingly alarming worldwide. In this paper, we firstly introduce the Improved BP Neural Network model (IBPNNM) based on entropy value theory in detail. Then we adopt Levenberg-Marquardt (LM) algorithm to achieve a higher speed and a lower error rate to overcome the shortcomings of the traditional BP algorithm as being slow to converge and easy to reach extreme minimum value. To illustrate the procedure of the proposed method, we apply IBPNNM to describe the flood disaster risk quantitatively in China by using statistical data respectively, including number of deaths, number of victims, collapse of housing, affected crop area, direct economic loss of 22 Areas in China in 1998. The results of IBPNNM are compared with the results of the matter-element analysis method to confirm that the proposed model is reasonable, effective and feasible.

Several Different Remote Sensing Image Classification Technology Analysis

Remote sensing (RS) image classification is an important content of RS research area. How to solve the classification of the multi-category image, avoid the existence of “same objects with different spectrums” and “different spectrums with same objects” phenomemon, and achieve certain accuracy requirement is a key question in RS application research, which has the very important practical significance especially in the military uffairs scout, geological survey and civil fields. By using the LANDSAT-7 ETM + RS image with the support of the project which investigated geology and mineral resources in the around 1:50 thousand regional Sepikou, Mulei County of Xinjiang in 2008, the improved BP neural network model is designed in this paper by the adaptive learning rate and the additional momentum, and compared with the traditional classification methods. Through the precision analysis by the error matrix, the result shows that the total classification accuracy which uses improved BP NN classification of RS image is 89.8%, and the classification accuracy of RS image is improved obviously, and is a kind of effective classification method.

Application of Improved BP Neural Network Model in Uplift Pressure Monitoring

The network structure, initial weights and initial thresholds were optimized to solve some problems, such as over-fitting and slow convergence rate in standard BP Neural Network. Combining the base seepage character of concrete dam, a uplift pressure monitoring model is established in this paper with measured data of a actual concrete dam . The advantage of the presented model is tested and validated by actual examples. It has positive significance in the actual application.

Remote Sensing Image Classification of the Improved BP NN

Remote sensing (RS) image classification plays the very important practical role in the geological survey and mineral exploration. The neural network (NN) technology is an important method for the RS image classification. But, the BP NN still has shortcomings, for example, the learning convergence rate is slow and the training process is easy to fall into the partial minimum. By taking the classification of 512×512 pixel experimental area of Mulei County of Xinjiang as the example, the improved BP NN model is designed in this article by the adaptive learning rate and the additional momentum, using the LANDSAT-7 ETM + RS images as the main data source. Through the precision analysis by the error matrix, the result shows that the total classification NNaccuracy which uses improved BP NN classification of RS image is 89.06%, and the Kappa coefficient is 85.53%, and the classification accuracy of RS image is improved obviously.