scholarly journals Analysis of User Personalized Retrieval of Multimedia Digital Archives Dependent on BP Neural Network Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Zhongke Wang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Probability Model ◽  
Optimal Solution ◽  
Implementation Process ◽  
Multimedia Retrieval ◽  
Digital Archives ◽  
Network Computing ◽  
Neural Network Algorithm ◽  
Neural Network Computing

This paper briefly introduces the characteristics of content-based multimedia retrieval under the information background, analyzes the implementation process of these technologies in the multimedia archives retrieval system including video and image information of digital archives, and points out that the content-based multimedia retrieval technology is bound to be organically combined with the traditional text retrieval methods. The information retrieval technologies in the past can only comply with the specific requirements of customers. Due to their characteristics of universality, they can hardly meet the demands of different environments, various purposes, and different times at the same time yet. Researchers have put forward personalized retrieval of multimedia files based on the BP neural network computing. In this way, the interest model of customers can be analyzed based on the characteristics of the different classification areas of users. Subsequently, the corresponding calculations are carried out, and the model is updated accordingly. Through the experiments, it is verified that the probability model put forward in this paper is the optimal solution to express the interest of customers and its changes.

Precision Power System Harmonic Analysis Algorithm Based on ABC-BPNN

2013 ◽  
Vol 483 ◽  
pp. 630-634
Author(s):  
Shu Chuan Gan ◽  
Ling Tang ◽  
Li Cao ◽  
Ying Gao Yue
Keyword(s):  
Neural Network ◽  
Power System ◽  
Harmonic Analysis ◽  
Bp Neural Network ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Optimal Solution ◽  
Bee Colony ◽  
Network Algorithm ◽  
Neural Network Algorithm

An algorithm of artificial colony algorithm to optimize the BP neural network algorithm was presented and used to analyze the harmonics of power system. The artificial bee colony algorithm global searching ability, convergence speed for the BP neural network algorithm for harmonic analysis is easy to fall into local optimal solution of the disadvantages, and the initial weights of the artificial bee colony algorithm also greatly enhance whole algorithm model generalization capability. This algorithm using MATLAB for Artificial bee colony algorithm and BP neural network algorithm simulation training toolbox found using artificial bee colony algorithm to optimize BP neural network algorithm converges faster results with greater accuracy, with better harmonic analysis results.

An optimized BP neural network model for teaching management evaluation

2020 ◽  
pp. 1-7
Author(s):  
Xinhua Yang ◽  
JiaJia Zhou ◽  
DaoQun Wen
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Bp Neural Network ◽  
Particle Swarm ◽  
Optimal Solution ◽  
University Teaching ◽  
Swarm Optimization ◽  
Teaching Management ◽  
Management Evaluation ◽  
Neural Network Algorithm

To improve the effectiveness and intelligence of university teaching management evaluation, the particle swarm optimization BP neural network algorithm is applied to the analysis of university teaching management evaluation data. BP neural network is used to model the evaluation index of teaching management, and then particle swarm optimization is used to optimize the weight and threshold of the neural network transfer function to ensure that the output of the BP neural network can obtain the global optimal solution. The experimental results show that the proposed algorithm has a good fit between the predicted value and the actual value of the evaluation object of teaching management in Colleges and universities, and has a strong promotion value.

Moisture Content of Crude Oil Based on BP Neural Network Algorithm to Predict in Oilfield

2012 ◽  
Vol 170-173 ◽  
pp. 1290-1293 ◽  
Author(s):  
Ying Ming Zhou ◽  
Qiu Shi Wang ◽  
Peng Wang ◽  
Li Na Yao
Keyword(s):  
Neural Network ◽  
Moisture Content ◽  
Crude Oil ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Implementation Process ◽  
Evaluation Index ◽  
Precision Control ◽  
Neural Network Algorithm ◽  
Bp Neural Network Model

This paper proposed new method of testing a moisture content of the crude oil which is based on BP neural network. It describes the principle of BP neural network model and calculation method to predict the moisture content of crude oil. The normalization of evaluation index and the implementation process of this method in computers. In the end, an application example of this method used in the process of practice and precision control requirements.

Research of Fault Diagnosis of Belt Conveyor Based on Fuzzy Neural Network

2014 ◽  
Vol 8 (1) ◽  
pp. 916-921
Author(s):  
Yuan Yuan ◽  
Wenjun Meng ◽  
Xiaoxia Sun
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Fault Location ◽  
Fuzzy Neural Network ◽  
Fuzzy Theory ◽  
Belt Conveyor ◽  
Fuzzy Neural ◽  
Neural Network Algorithm ◽  
Network Fault

To address deficiencies in the process of fault diagnosis of belt conveyor, this study uses a BP neural network algorithm combined with fuzzy theory to provide an intelligent fault diagnosis method for belt conveyor and to establish a BP neural network fault diagnosis model with a predictive function. Matlab is used to simulate the fuzzy BP neural network fault diagnosis of the belt conveyor. Results show that the fuzzy neural network can filter out unnecessary information; save time and space; and improve the fault diagnosis recognition, classification, and fault location capabilities of belt conveyor. The proposed model has high practical value for engineering.

scholarly journals A Method of Ore Blending Based on the Quality of Beneficiation and Its Application in a Concentrator

2021 ◽  
Vol 11 (11) ◽  
pp. 5092
Author(s):  
Bingyu Liu ◽  
Dingsen Zhang ◽  
Xianwen Gao
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Industrial Test ◽  
Modeling Method ◽  
Daily Work ◽  
Production Site ◽  
Neural Network Algorithm ◽  
The Relationship ◽  
Ore Dressing

Ore blending is an essential part of daily work in the concentrator. Qualified ore dressing products can make the ore dressing more smoothly. The existing ore blending modeling usually only considers the quality of ore blending products and ignores the effect of ore blending on ore dressing. This research proposes an ore blending modeling method based on the quality of the beneficiation concentrate. The relationship between the properties of ore blending products and the total concentrate recovery is fitted by the ABC-BP neural network algorithm, taken as the optimization goal to guarantee the quality of ore dressing products at the source. The ore blending system was developed and operated stably on the production site. The industrial test and actual production results have proved the effectiveness and reliability of this method.

Application of BP Neural Network PID Algorithm in Refrigerated Space Temperature Control

2014 ◽  
Vol 599-601 ◽  
pp. 827-830 ◽  
Author(s):  
Wei Tian ◽  
Yi Zhun Peng ◽  
Pan Wang ◽  
Xiao Yu Wang
Keyword(s):  
Neural Network ◽  
Temperature Control ◽  
Bp Neural Network ◽  
Pid Control ◽  
Bp Algorithm ◽  
Pid Algorithm ◽  
Controller Performance ◽  
Neural Network Algorithm ◽  
Adaptive Ability ◽  
Neural Network Pid

Taking the temperature control of a refrigerated space as example, this paper designs a controller which is based on traditional PID operation and BP neural network algorithm. It has better steady-state precision and adaptive ability. Firstly, the article introduces the concepts of the refrigerated space, PID and BP algorithm. Then, the temperature control of refrigerated space is simulated in MATLAB. The PID parameters will be adjusted by simulation in BP Neural Network. The PID control parameters could be created real-time online, which makes the controller performance best.

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