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International Journal of Social Science and Education Research 2021年第1期4卷 104-109页

作者： Yue Wang

In order to solve the relatively lack of research on the wind speed prediction of domestic transmission lines. In this paper, the time series analysis method is used to predict the wind speed of the transmission line, and the prediction accuracy is not high. By consulting related references, this paper proposes a combination of bp neural network algorithm and genetic algorithm GA-bp neural network algorithm combined with time series analysis method to obtain high-precision wind speed prediction algorithm. Finally, using the advantages of MapReduce distributed operation mechanism, the GA-bp neural network algorithm is implemented on MapReduce. The simulation results show that the algorithm has a great improvement in prediction accuracy compared with the time series analysis method.

关键词： Transmission line bp neural network algorithm Genetic algorithm MapReduce

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Research on Food Demand Prediction algorithm Based on Supply Chain Management

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Advance Journal of Food Science and Technology 2016年第12期11卷 832-836页

作者： Zhu Jianbin School of Business Administration Research Center of Cluster and Enterprise Development Jiangxi University of Finance and Economics Nanchang Jiangxi 330013 China

An improved bp neural network algorithm for food demand prediction based on supply chain management is presented to realize market and sale management target effectively for food enterprises. First, the working principle of bp neural network algorithm is analyzed to explore the root reasons of its low convergence speed; Second, the paper integrates genetic algorithm with bp algorithm to present a new algorithm, then improves it through encoding chromosome, formatting fitness function, designing selection operator, redesigning crossover operator, designing mutation operator, integrating bp algorithm and optimal individual, improving calculation process step by step; Finally, a supply chain of a food enterprise is taken for experimental sample to illustrate the calculation performance of the improved algorithm and the simulation results indicate that the improved algorithm not only can solve the problem of low convergence speed, but also can improve the demand prediction accuracy and can be used for predicting supply chain demand for food enterprises practically.

关键词： bp neural network algorithm food demand prediction genetic algorithm supply chain management

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The Mechanism of the Role of Big Data Knowledge Management in the Development of Enterprise Innovation

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2023年第6期14卷 1364-1372页

作者： Yan, Guangyu Ma, Rui Chengdu Polytech Dept Sci Res Chengdu 610041 Peoples R China Chengdu Univ Tradit Chinese Med Hlth & Rehabil Coll Chengdu 611137 Peoples R China

The effectiveness and efficiency of enterprise knowledge management depends on the effectiveness and efficiency of the enterprise's implementation of knowledge management. Big data technology can collect, analyse and apply the massive amount of data in an organisation to support the implementation of knowledge management. Therefore, exploring the role of big-data knowledge management in the development of enterprise innovation will help enterprises to better implement knowledge management. Based on this, the study aims to propose a model for predicting big data knowledge management and enterprise innovation development for high-tech enterprises in China. The study firstly used Principal Component Analysis (PCA) to decrease the dimensionality of the model, and then used the particle swarm algorithm to optimize bp neural network (PSO-bp). network (PSO-bp) was used to evaluate enterprise knowledge management and enterprise innovation development. The results of the study show that the absolute values of the relative errors of the pre-processed model do not exceed the 5% threshold, and only the relative errors of some indicators are relatively large, such as X5 and X7, with values of 4.5% and -3.8%, indicating that the model has a good performance in predicting the innovation effect of enterprises.

关键词： Big data knowledge management bp neural network algorithm enterprise innovation development principal component analysis particle swarm optimization algorithm correlation analysis

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Improved bp Classifier via Distance for Sample Reduction

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电脑学刊 2019年第2期30卷 1-13页

作者： Jia Wen Jia Deng Peng-Fei Liu Hong-Jun Wang

The bp neural network algorithm is a commonly used technique in the classification field of pattern recognition, which iterates training samples continuously to obtain the decision boundary, until the error reaches the convergence precision. However, due to the extensive redundancy of training samples, this carpet-like training method can easily lead to slow network convergence and low classification performance. In this paper, from the view of sample, a novel bp training method called improved bp Classifier algorithm using Distance for Sample Reduction (DRbp) is proposed. Through the sample weight to get the new training sample, which are the near of the classification interface like support vector, and then iterative training bp classifier to solve the classification issue. The main function of this method is to keep the original sample information, realize the sample reduction, and improve the generalization ability of the bp neural network. First, point sets is used to visualize the training process. And then use 10 sets of the University of California Irvine (UCI) to verify the effectiveness and superiority of our experiments. Finally through Cross-validation, the classification error rate of 10 sets of UCI datasets tested by DRbp training method is lower than that of standard bp neural network (STbp). So the result of the experiment showed that the method of the combined distance sample reduce with bp network can significantly enhance the generalization ability of bp network.

关键词： bp neural network algorithm classification interface sample reduce support vector

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Computer Communication network Fault Detection Based on Improved neural network algorithm

ELECTRICA

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ELECTRICA 2022年第3期22卷 351-357页

作者： Sun, Dong Chopra, Pooja Bhola, Jyoti Neware, Rahul Henan Polytech Univ Sch Comp Sci & Technol Xinxiang Henan Peoples R China GNA Univ Phagwara Punjab India Natl Inst Technol Elect & Commun Engn Dept Hamirpur India Hogskulen Vestlandet Dept Comp Math & Phys Bergen Norway Lovely Profess Univ Sch Comp Applicat Jalandhar Punjab India

To detect computer communication network failures, a computer communication network fault detection based on an improved neural network algorithm is proposed. A network fault diagnosis example is used to verify the effectiveness of the method. There are many network failure phenomena. Here, the author selected 13 network fault information parameters for a comprehensive diagnosis of network failures. The author designed a three-layer backpropagation (bp) neural network. There are 13 nodes in the input layer, corresponding to the above 13 network fault parameter information. The output layer has three nodes, which output the fault code sequence. The training of the network uses the trainlm0 function. The performance function uses the mean square error performance function mse0 and set e = 0.001;the network learning rate is set to a = 0.05. The author selects 100 failure data as the training set for network training and selects 10 sets of samples as the test set. The experimental data shows that after the network has been trained 25 times, the output error reaches the set precision e. After training the bp network using this algorithm 140 times, the output error reaches the set precision e. This method effectively improves the effectiveness and accuracy of S network fault diagnosis.

关键词： network failure improved neural network algorithm bp neural network algorithm fault detection

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Time Series Analysis in Security Analysis

Time Series Analysis in Security Analysis

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2011 4th IEEE International Conference on Computer Science and Information Technology(ICCSIT 2011)

作者： Yuning Hao School of Mathematical Sciences, major in statistics Beijing Normal University Beijing China

This paper introduces the definition of time series analysis and its characteristics. Have developed in recent years, time series analysis is quantitative forecasting method, which is particularly applicable to economic time series. The ARIMA model and the MATLAB software are used to research the time series of stock market in China. Time series analysis is fully demonstrated in the analysis of the performance of stock market. Volatility of securities markets has been the focus of academic research. China's securities market is still relatively young, so unusual fluctuations in the stock market appeared one after another, and even the rough conditions is not surprising. It is not terrible that there are always the bull and bear markets. The spike is frightening. Volatility in the stock market not only occurs in China. However, it is certainly a unique that the volatility in the domestic stock market is very high. The Yijing said: The yin and yang are of that Road. If yin and yang are regarded as conflicts, Tao is to resolve the conflict. Tao is the principles and laws of the universe. Therefore, it is of great significance for government regulators, academics and investors to grasp the laws of stock market volatility.

关键词： time series analysis, time trends time series models autocorrelation function partial autocorrelation function bp neural network algorithm MATLAB software

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Research and prevention of illegal intrusion of digital television network

Research and prevention of illegal intrusion of digital tele...

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2015 International Conference on Electric, Electronic and Control Engineering (ICEECE 2015)

作者： Xiangfeng Hu College of Physics Tonghua Normal University

Digital television is now developing rapidly in *** momentum is thrilling,which also brings numerous network security *** paper carries out a study on illegal intrusion of digital television *** from detection model,it proposes the intrusion detection method based on ESTQ and the intrusion detection method based on expert system and bp neural network algorithm on the basis of the analysis on potential safety hazards of digital television *** also designs a realization approach of linkage alarm in method research and analysis so as to lay a theoretical foundation for the sound development of radio and television undertakings in China.

关键词： illegal intrusion detection alarming device ESTQ four components expert system bp neural network algorithm

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The Application of Forecasting algorithms on Electric Vehicle Power Load

The Application of Forecasting Algorithms on Electric Vehicl...

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IEEE International Conference on Mechatronics and Automation

作者： Shijun Wang Guobin Xue Chang Ping Dinggang Wang Feng You Tao Jiang Technology Research Institute Gansu Electric Power Company Lanzhou Gansu Province China Electric Transmission &Transformer co. TLD Gansu Electric Power Company Lanzhou Gansu Province China

The autoregressive model algorithm (ARA) of time series analysis and reverse transmission neural network algorithm (bpA) of machine learning were used in this paper, and applied the load forecasting of electric vehicle (EV), through studying and analysising of different algorithms, aiming at certain domestic city electric vehicle charging station has carried on the short-term load forecast, compared of prediction results of the two ways, the simulation results show that different algorithm also have different effects in the short-term load forecasting, therefore, according to different system to select appropriate learning algorithm is particularly important.

关键词： Autoregressive model algorithm bp neural network algorithm Electric car Load forecasting

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The forecasting of Shanghai Index trend Based on Genetic algorithm and Back Propagation Artificial neural network algorithm

The forecasting of Shanghai Index trend Based on Genetic Alg...

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The 6th International Conference on Computer Science & Education(第六届国际计算机新科技与教育学术会议 ICCSE 2011)

作者： LI Yizhen Zeng Wenhua Lin ling Wu jun Lu Gang Software School Xia Men University Xiamen China

this thesis presents a bp Artificial neural network prediction modeling method for forecasting the trend of Shanghai index, and then uses the genetic algorithm to optimize the bp network parameters, weight and structure. The forecasting results show that the optimization algorithm not only avoids bp algorithm into a local minimum point and the problems of slow convergence, but also overcome the GA Shortcomings such as the search time too long and search speed too slow caused by in a similar form of exhaustive search for optimal solution. In the stock market of such a complicated nonlinear stochastic system modeling, this modeling method has high application value.

关键词： bp neural network algorithm GA Stock prediction Shanghai index

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Empirical Research of Evaluating Training Base Teaching for Higher Vocation Education

Empirical Research of Evaluating Training Base Teaching for ...

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作者： Zhaohui Zheng Xuehui Pei Mingfu Liao Academic Affairs Office of Jiangxi Vocational and Technical College of Information E-commerce & Management Department of Jiangxi Vocational and Technical College of Information Foreign language college of Jiangxi agriculture university

Training base teaching evaluation of higher vocation education is one of the difficult and hot research fields in higher education management. Using bp neural network algorithm, a new clustering segmentation algorithm is presented. Firstly, According to the characteristics of training base teaching evaluation of higher vocation, a new evaluation indicator system is constructed from the perspectives of training preparation, specific training, training administration, training effects;Second, the paper improves the original bp algorithm through adjusting the value of momentum factor, taking dynamic strategy of momentum factor and adaptive change of algorithm step to speed up the calculation;Finally, the empirical results shows that improved model has high evaluation accuracy when used to evaluate training base teaching of higher vocation education and can improve process stability and speed greatly.

关键词： Higher vocation education Training base teaching evaluation bp neural network algorithm Evaluation indicator system

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