scholarly journals Research on Economic Mathematical Analysis and Construction Model of Prefabricated Building Structure Based on Improved Neural Network Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Xin Lin
Keyword(s):  
Neural Network ◽  
Learning Process ◽  
Mathematical Analysis ◽  
Fitness Function ◽  
Weight Vector ◽  
Building Structure ◽  
Analysis Model ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Prefabricated Building

In this article, a mathematical analysis model of economics of prefabricated building structure based on improved neural network algorithm is proposed in order to solve the low analysis accuracy in traditional methods. Firstly, by means of analyzing the costs of materials, labor, and equipment, the economic characteristics of the cost of fabricated building structures are determined. Secondly, the single neuron is analyzed and the weight coefficient is adjusted in accordance with the multilayer neural network structure, so as to minimize the construction error of the economic analysis model of the assembled building structure. Meanwhile, the weight vector is obtained, error-weighted square sum is calculated through choosing an adaptive filter and obtained, and the weight vector is updated by the least squares algorithm. Thirdly, the neural network algorithm training and learning process is designed and improved, the dependent variable is selected, the number of input points is determined, and then, the training and learning process of the improved neural network algorithm is completed. Finally, a fitness function is set to measure the authenticity of dataset, which is further defined as a combination of different weights to construct an economic mathematical analysis model. The experimental results indicate that the analysis results of this method can reach an accuracy up to 96%, so it has a broader application prospect in low-rise buildings.

Nuclear Magnetic Image Segmentation of Rectal Cancer Based on Improved Genetic Neural Network

2020 ◽  
Vol 10 (7) ◽  
pp. 1644-1653
Author(s):  
Danyang Li ◽  
Yumei Sun ◽  
Wanqing Liu ◽  
Bing Hu ◽  
Jianlin Wu ◽  
...  
Keyword(s):  
Neural Network ◽  
Rectal Cancer ◽  
Image Segmentation ◽  
Fitness Function ◽  
Segmentation Method ◽  
Magnetic Image ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Genetic Neural Network ◽  
Nuclear Magnetic

Image segmentation is the basis of image analysis and understanding, and has an unshakable position in the field of computer vision. In order to improve the accuracy of nuclear magnetic image segmentation of rectal cancer, this paper proposes an improved genetic neural network algorithm for the problems of traditional BP neural network algorithm. In order to enhance the network performance, this paper improves the genetic neural network from the two aspects of fitness function and genetic operator, which makes the training speed and convergence precision greatly improved. Target samples were analyzed by image histogram analysis, and the improved genetic neural network was used to learn the samples to obtain the training network. Taking the pixel matrix of the image as the input vector, it is put into the trained network for classification, and finally the segmentation is realized. The simulation experiment proves that compared with the classical image segmentation method, the improved genetic neural network image segmentation method has a good segmentation effect and is a feasible image segmentation method.

A RBF fuzzy logic neural network algorithm for construction resource scheduling

2021 ◽  
pp. 1-9
Author(s):  
Xiaobing Yu
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Construction Projects ◽  
Resource Scheduling ◽  
Road Transportation ◽  
Building Projects ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Intelligent Technology ◽  
Prefabricated Building

Rapid progress has been made in the intelligent technology of prefabricated buildings in recent years, and the related scheduling in many fields such as component production, workshop assembly, and road transportation is used for the optimization of resources. In this paper, the prefabricated building project is taken as the research objective to analyze the constraint conditions between prefabricated building projects in detail. It is proposed that the radial basis function (RBF) fuzzy logic neural network algorithm should be introduced into the optimization of building resource scheduling. Finally, the results of the experimental analysis suggest that the proposed method can effectively address the problem of resource scheduling in the prefabricated construction project, which can also provide a reference for the managers of prefabricated construction projects.

scholarly journals Music Emotion Analysis Based on PSO-BP Neural Network and Big Data Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Chen Xi
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Emotional Expression ◽  
Bp Neural Network ◽  
Influence Factors ◽  
Big Data Analysis ◽  
Emotion Expression ◽  
Analysis Model ◽  
Network Algorithm ◽  
Neural Network Algorithm

The current music teaching can effectively improve students’ music emotional expression indirectly. How to use the PSO-BP neural network to realize the quantitative research of music emotional expression is the current development trend. Based on this, this paper studies the influence factors of music emotion expression based on PSO-BP neural network and big data analysis. Firstly, a music emotion expression analysis model based on PSO-BP neural network algorithm is proposed. The autocorrelation function is used to simulate the emotion expression information in music. Through the maximum value of the autocorrelation function curve in the detection process, the vocal music signal is restored, and then the emotion expressed is analyzed. Secondly, the influence factors of PSO-BP neural network algorithm in music emotion expression are analyzed. The improved PSO-BP neural network algorithm and multidimensional data model are used for comprehensive analysis to accurately analyze the emotion in music expression, and the fuzzy evaluation method and analytic hierarchy process are used for quality evaluation. Finally, the validity of the music emotion analysis model is verified by many experiments.

scholarly journals Analysis of Industry Convergence Based on Improved Neural Network

2021 ◽  
Author(s):  
Nan Ma
Keyword(s):  
Neural Network ◽  
Convergence Analysis ◽  
Information Age ◽  
Analysis Model ◽  
Model Based ◽  
Industry Convergence ◽  
Network Algorithm ◽  
The Neural Network ◽  
Group Competition ◽  
Neural Network Algorithm

Abstract Economic growth in the information age is no longer a stage driven by unipolarity. It has entered a multi-polar driving stage characterized by integration, fusion, and integrated development on a larger scale between regions, and the trend of group competition with urban agglomerations as carriers has become increasingly obvious. This paper improves the neural network algorithm based on the needs of industrial economic integration in the digital age, and proposes an industry convergence analysis model based on the improved neural network algorithm. Moreover, this article combines industry models to analyze actual needs and constructs an industry convergence analysis model based on improved neural networks, and analyzes the integration of different industries. In addition, this article conducts experiments through multiple sets of data, and combines the neural network model of this article to conduct research. Through experimental research, we know that the model constructed in this paper can play an important role in the analysis of industry convergence.

Simulation Analysis of Collaborative Efficiency Improvement of Innovative Talents Management in Colleges and Universities Based on BP Neural Network

2021 ◽  
Vol 7 (5) ◽  
pp. 4449-4462
Author(s):  
Xiyin Chang ◽  
Yuchun Sun
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Simulation Analysis ◽  
Talent Management ◽  
Colleges And Universities ◽  
Management Model ◽  
Analysis Model ◽  
Calculation Time ◽  
Network Algorithm ◽  
Neural Network Algorithm

Objectives: In recent years, it is more and more difficult to manage innovative talents. In order to improve the collaborative efficiency of innovative talents management, this paper presents a simulation analysis of collaborative efficiency of innovative talents management in Colleges and Universities Based on BP neural network algorithm. Methods: Data simulation technology is used to establish talent management model. This model puts forward the optimization scheme from the algorithm flow, and improves the synergy of talent management by using data transformation technology. This model is analyzed from two aspects of universities and talents. BP neural network algorithm is added to the calculation of management efficiency to realize the sequence optimization of data. Results: In order to test the authenticity and efficiency of the algorithm in the talent management model, a comparative experiment is set up to analyze the results. The test results show that the accuracy of the optimized data analysis model is generally above 95%, while the accuracy of the traditional algorithm is generally below 80%, the collaborative efficiency calculation time of talent management model is the shortest, averaging only about 15 seconds; the traditional model calculation time is very unstable, from short 12 seconds to long 45 seconds, the calculation span is very large, and the accuracy rate is low. Conclusion: The research shows that BP neural network algorithm can improve the synergy of management and optimize the management mode of innovative talents, which is worthy of further promotion.

A NEURAL NETWORK ALGORITHM FOR DENSITY MEASUREMENT OF MULTIPHASE FLOW

2012 ◽  
Vol 24 (2) ◽  
pp. 89-103 ◽  
Author(s):  
Nabeel Al-Rawahi ◽  
Mahmoud Meribout ◽  
Ahmed Al-Naamany ◽  
Ali Al-Bimani ◽  
Adel Meribout
Keyword(s):  
Neural Network ◽  
Multiphase Flow ◽  
Density Measurement ◽  
Network Algorithm ◽  
Neural Network Algorithm
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