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

2021 ◽  
Vol 2021 ◽  
pp. 1-1
Author(s):  
Xin Lin
Keyword(s):  
Neural Network ◽  
Mathematical Analysis ◽  
Building Structure ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Construction Model ◽  
Prefabricated Building

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.

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.

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

Logistics demand forecasting model based on improved neural network algorithm

2020 ◽  
pp. 1-11
Author(s):  
Hongjiang Ma ◽  
Xu Luo
Keyword(s):  
Neural Network ◽  
Influencing Factor ◽  
Demand Forecasting ◽  
Forecasting Model ◽  
Logistics System ◽  
Model Based ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Demand Forecasting Model ◽  
Logistics Demand

The irrationality between the procurement and distribution of the logistics system increases unnecessary circulation links and greatly reduces logistics efficiency, which not only causes a waste of transportation resources, but also increases logistics costs. In order to improve the operation efficiency of the logistics system, based on the improved neural network algorithm, this paper combines the logistic regression algorithm to construct a logistics demand forecasting model based on the improved neural network algorithm. Moreover, according to the characteristics of the complexity of the data in the data mining task itself, this article optimizes the ladder network structure, and combines its supervisory decision-making part with the shallow network to make the model more suitable for logistics demand forecasting. In addition, this paper analyzes the performance of the model based on examples and uses the grey relational analysis method to give the degree of correlation between each influencing factor and logistics demand. The research results show that the model constructed in this paper is reasonable and can be analyzed from a practical perspective.

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