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 fuzzy logic neural network algorithm for compressor crankshaft-rolling bearing system optimization

2021 ◽  
pp. 1-7
Author(s):  
Zheng Zhang ◽  
Jianrong Zheng
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Dynamic Analysis ◽  
Weighted Average ◽  
Rolling Bearing ◽  
System Optimization ◽  
Simulation Software ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Bearing System

Taking the crankshaft-rolling bearing system in a certain type of compressor as the research objective, dynamic analysis software is used to conduct detailed dynamic analysis and optimal design under the rated power of the compressor. Using Hertz mathematical formula and the analysis method of the superstatic orientation problem, the relationship expression between the bearing force and deformation of the rolling bearing is solved, and the dynamic analysis model of the elastic crankshaft-rolling bearing system is constructed in the simulation software ADAMS. The weighted average amplitude of the center of the neck between the main bearings is used as the target, and the center line of the compressor cylinder is selected as the design variable. Finally, an example analysis shows that by introducing the fuzzy logic neural network algorithm into the compressor crankshaft-rolling bearing system design, the optimal solution between the design variables and the objective function can be obtained, which is of great significance to the subsequent compressor dynamic design.

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 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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