Modified neural network algorithm based robust design of AVR system using the Kharitonov theorem

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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Stock Trend Prediction of Communication Industry Based on LSTM Neural Network Algorithm and Research of Industry Development Strategy

2021 The 4th International Conference on Computers in Management and Business ◽

10.1145/3450588.3450606 ◽

2021 ◽

Author(s):

Boyi Wan ◽

Yun Shen

Keyword(s):

Neural Network ◽

Development Strategy ◽

Trend Prediction ◽

Industry Development ◽

Communication Industry ◽

Network Algorithm ◽

Neural Network Algorithm

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Modeling the SOFC by BP neural network algorithm

International Journal of Hydrogen Energy ◽

10.1016/j.ijhydene.2021.03.132 ◽

2021 ◽

Author(s):

Shaohui Song ◽

Xingyu Xiong ◽

Xin Wu ◽

Zhenzhong Xue

Keyword(s):

Neural Network ◽

Bp Neural Network ◽

Network Algorithm ◽

Neural Network Algorithm

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

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189974 ◽

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.

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