scholarly journals An Evaluation Model for Tailings Storage Facilities Using Improved Neural Networks and Fuzzy Mathematics

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Sen Tian ◽  
Jianhong Chen
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Evaluation Model ◽  
Back Propagation ◽  
Safety Evaluation ◽  
Evaluation Index System ◽  
Analytic Hierarchy ◽  
Operation Rate ◽  
Network Methods ◽  
Neural Network Algorithm

With the development of mine industry, tailings storage facility (TSF), as the important facility of mining, has attracted increasing attention for its safety problems. However, the problems of low accuracy and slow operation rate often occur in current TSF safety evaluation models. This paper establishes a reasonable TSF safety evaluation index system and puts forward a new TSF safety evaluation model by combining the theories for the analytic hierarchy process (AHP) and improved back-propagation (BP) neural network algorithm. The varying proportions of cross validation were calculated, demonstrating that this method has better evaluation performance with higher learning efficiency and faster convergence speed and avoids the oscillation in the training process in traditional BP neural network method and other primary neural network methods. The entire analysis shows the combination of the two methods increases the accuracy and reliability of the safety evaluation, and it can be well applied in the TSF safety evaluation.

The Model of Evaluating Teaching Quality Based on BP Neural Network Algorithm

2015 ◽  
Vol 719-720 ◽  
pp. 1297-1301
Author(s):  
Lei Bai ◽  
Xiao Xin Guo
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Quality Evaluation ◽  
Hot Spot ◽  
Evaluation Model ◽  
Teaching Quality ◽  
Research Field ◽  
Evaluation Index System ◽  
Evaluation Index ◽  
Neural Network Algorithm

Teaching quality evaluation plays a key role for universities to improve its teaching quality and becomes a hot spot research field for related researchers. In this paper, we established the evaluation model of teaching quality based on BP neural network. Firstly an evaluation index system of teaching quality is designed. Then, according to the system we design the structure of BP neural network, determine the parameters and give the algorithm description. Finally, we program and verify the validity of the model in MATLAB environment. The experimental results show that the model can evaluate teaching quality practically by the evaluation index.

Researches on the Network Security Evaluation Method Based on BP Neural Network

2014 ◽  
Vol 686 ◽  
pp. 470-473 ◽  
Author(s):  
Yi Bin Zhang ◽  
Ze Quan Yan
Keyword(s):  
Neural Network ◽  
Network Security ◽  
Bp Neural Network ◽  
Evaluation Method ◽  
Computer Network ◽  
Evaluation Model ◽  
Evaluation Index System ◽  
Security Evaluation ◽  
Computer Network Security ◽  
Neural Network Algorithm

This paper first describes the basic theory of BP neural network algorithm, defects and improved methods, establishes a computer network security evaluation index system, explores the computer network security evaluation method based on BP neural network, and has designed to build the evaluation model, and shows that the method is feasible through the MATLAB simulation experiments.

Research on the Safety Evaluation of Large Recreation Facilities Based on BP Neural Network

2014 ◽  
Vol 522-524 ◽  
pp. 881-886
Author(s):  
Yu Zhang ◽  
Zhi Rong Wang ◽  
Qing Qing Zuo ◽  
Xin Dong Zhang ◽  
Xiang Dong Li
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Human Factor ◽  
Evaluation Model ◽  
Safety Evaluation ◽  
Qualitative Evaluation ◽  
Evaluation Index System ◽  
Model Based ◽  
Network Training ◽  
Recreation Facilities

A safety evaluation index system regarding to the current safety situation of large recreation facilities in China is established. 13 secondary standard items are built by considerring human factor, equipment factor, environment factor and management factor. The existing safety evaluation of large recreation facilities are conducted by qualitative evaluation methods with highly fuzziness. The evaluation results are uncertain. After the network training, a safety evaluation model based on BP neural network is built. It can reduce the subjectivity of qualitative evaluation effectively with more scientific and objective results. Through the model based on BP neural network, the present safety situation of one large amusement facility is evaluated. The evaluation result is consistent with the actual situation. The method based on BP neural network in the paper provides a new method for safety evaluation of large recreation facilities.

scholarly journals RESEARCH ON ATHLETES’ PSYCHOLOGICAL REGULATION ABILITY BASED ON BACK PROPAGATION (BP) NEURAL NETWORK ALGORITHM

2021 ◽  
Vol 27 (spe2) ◽  
pp. 83-86
Author(s):  
Yun Tan ◽  
Guoqing Zhang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Psychological Control ◽  
Back Propagation ◽  
Rotation Matrix ◽  
Psychological State ◽  
Advantages And Disadvantages ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Game State

ABSTRACT Athletes’ psychological control ability directly affects competitions. Therefore, it is necessary to supervise the athletes’ game psychology. Athletes’ game state supervision model is constructed through the facial information extraction algorithm. The homography matrix and the calculation method are introduced. Then, two methods are introduced to solve the rotation matrix from the homography matrix. After the rotation matrix is solved, the method of obtaining the facial rotation angle from the rotation matrix is introduced. The two methods are compared in the simulation data, and the advantages and disadvantages of each algorithm are analyzed to determine the method used in this paper. The experimental results show that the model prediction accuracy reaches 70%, which can effectively supervise the psychological state of athletes. This research study is of great significance to improve the performance of athletes in competitions and improve the application of back propagation (BP) neural network algorithm.

scholarly journals An Evaluation Model for the Innovation and Entrepreneurship Thinking Ability of College Students Based on Neural Network

2021 ◽  
Vol 16 (02) ◽  
pp. 188
Author(s):  
Feng Zhang ◽  
Limin Xi
Keyword(s):  
Neural Network ◽  
College Students ◽  
Comprehensive Evaluation ◽  
Evaluation Model ◽  
Evaluation Index System ◽  
Analytic Hierarchy ◽  
Thinking Ability ◽  
Fuzzy Neural ◽  
Knowledge Rules ◽  
Innovation And Entrepreneurship

Mass innovation and entrepreneurship (I&E) is a national campaign in China. In this context, it is important to encourage college students to engage in I&E activities, and this calls for accurate and comprehensive evaluation of their I&E thinking ability. Therefore, this paper proposes an evaluation model for the I&E thinking ability of college students based on neural network (NN). Firstly, a reasonable evaluation index system was created for the I&E thinking ability of college students, and the evaluation indices were preprocessed through fuzzy analytic hierarchy process (AHP). Then, a fuzzy neural network (FNN) was constructed based on GA rule optimization and the specific steps of the algorithm were given. Moreover, a few representative rules were selected by GA based on uncertain fuzzy knowledge rules, a 4-layer NN model with fuzzy inputs and outputs was established, and the evaluation flow of the I&E thinking ability of college students was proposed. Finally, the effectiveness of the proposed model was verified through experiments. The research results of this paper provide a reference for the application of NN in the field of ability evaluation.

Research and Realization of Hardware Back-Propagation Neural Network Based on FPGA

2013 ◽  
Vol 333-335 ◽  
pp. 2469-2474
Author(s):  
Fei Guo ◽  
Xiao Luo
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Variable Structure ◽  
Maximum Error ◽  
Back Propagation Neural Network ◽  
Neural Network Modeling ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Timing Simulation

In order to meet the requirements of real-time and embedded of industrial field, a reconfigurable Back-Propagation neural network based on FPGA has been implemented on Xilinx's Spartan-3E (XC3S250E) chip which has 250000 gate. First the optimal network structure and weights were gotten by a variable structure of BP neural network algorithm. Then an improved hardware approaching method of excitation function was put forward, and the maximum error was 1.58% by simulation and comparative analysis on the error. Finally hardware co-imitation and timing simulation was token based on a reasonable choice of data accuracy, and then the hardware BP neural network algorithm was been downloaded and implemented on FPGA. This method has better accuracy and speed, it is an effective method of BP neural network modeling based on hardware, and lays the foundation for the hardware realization of other neural network and embedded image processing.

Concentration Polarization BP Model of Nanofiltration Separation

2011 ◽  
Vol 305 ◽  
pp. 247-250
Author(s):  
Qing Yang ◽  
Xin Qiu ◽  
Xiang Shen
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Concentration Polarization ◽  
Back Propagation ◽  
Separation Effect ◽  
Membrane Flux ◽  
Neural Network Algorithm ◽  
Set Up ◽  
Bp Model

In order to effectively control the influence of Concentration Polarization (CP) during the nanofiltration separating wastewater process, this study applied parameters characterization of membrane flux attenuation coefficient (mwt) and the Back-propagation (BP) neural network algorithm to simulate the development rules of CP and membrane pollution, set up CP BP Model of Nanofiltration Separation, based on the tested data of NF90. The correlation coefficient between simulation and test of the simulation BP model was over 0.99, with the absoluteness error below 1.5%. According to the model’s prediction, the separation effect of nanofiltration technology become attenuate with running time increasing in nanofiltration separating wastewater process. mwtstart raised obviously within first 0.5h in operation and stay stable after 1h. It was advised to appropriately maintain u>0.2m/s for NF90 membrane effectively controlling mwt<0.1.

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