scholarly journals Fuzzy Neural Network Algorithm in Improving Electrical Engineering Control System

2021 ◽  
Vol 2074 (1) ◽  
pp. 012080
Author(s):  
Xiaotao Tian
Keyword(s):  
Neural Network ◽  
Control System ◽  
Electrical Engineering ◽  
Electrical Control ◽  
Intelligent Control System ◽  
Engineering Control ◽  
Network Algorithm ◽  
Fuzzy Neural ◽  
Artificial Intelligence Technology ◽  
Neural Network Algorithm

Abstract In the current era of rapid development of big data technology and artificial intelligence technology, China’s comprehensive national strength has also been significantly enhanced, the rapid progress of science and technology makes the electrical engineering control system whether in terms of efficiency and quality of the application, its electrical engineering control system development is gradually improved with the support of high-tech. Based on artificial intelligence technology, neural network algorithms and improved neural network algorithms are proposed to improve the original electrical engineering control system. In this paper, the S electrical engineering control system is the main research object, and its addition of fuzzy neural network algorithm to improve the study. Firstly, on the basis of a simple description of S electrical control system, the research status of the main partition control blocks of S electrical control system is analyzed. Secondly, an improved intelligent control system, including intelligent service interruption system and central electrical control system, is proposed to design an improved electrical engineering control system based on neural network algorithm through the operation principle of sensors and the study of network communication technology. Based on the above research basis, the effectiveness and practicality of the proposed intelligent electrical engineering control system are verified by analyzing the effects of the proposed intelligent electrical engineering control system in real life. The experimental results show that although there are still many problems in the intelligent control system of three-phase electrical engineering at this stage, innovation and technological progress will continuously improve the comprehensiveness and intelligence level of the system.

Fuzzy neural network control algorithm for asymmetric building structure with active tuned mass damper

2020 ◽  
Vol 26 (21-22) ◽  
pp. 2037-2049
Author(s):  
Xiao Yan ◽  
Zhao-Dong Xu ◽  
Qing-Xuan Shi
Keyword(s):  
Neural Network ◽  
Control System ◽  
Fuzzy Neural Network ◽  
Linear Quadratic Regulator ◽  
Tuned Mass Damper ◽  
Linear Quadratic ◽  
Mass Damper ◽  
Fuzzy Neural ◽  
Neural Network Algorithm ◽  
Damper Control

Asymmetric structures experience torsional effects when subjected to seismic excitation. The resulting rotation will further aggravate the damage of the structure. A mathematical model is developed to study the translation and rotation response of the structure during seismic excitation. The motion equations of the structures which cover the translation and rotation are obtained by the theoretical derivations and calculations. Through the simulated computation, the translation and rotation response of the structure with the uncontrolled system, the tuned mass damper control system, and active tuned mass damper control system using linear quadratic regulator algorithm are compared to verify the effectiveness of the proposed active control system. In addition, the linear quadratic regulator and fuzzy neural network algorithm are used to the active tuned mass damper control system as a contrast group to study the response of the structure with different active control method. It can be concluded that the structure response has a significant reduction by using active tuned mass damper control system. Furthermore, it can be also found that fuzzy neural network algorithm can replace the linear quadratic regulator algorithm in an active control system. Because fuzzy neural network algorithm can control the process on an uncertain mathematical model, it has more potential in practical applications than the linear quadratic regulator control method.

scholarly journals EQUIPMENT PRICE FORECAST BASED ON T-S FUZZY NEURAL NETWORK

2018 ◽  
Vol 48 (4) ◽  
pp. 305-309
Author(s):  
G. P. JIANG ◽  
L. XIE ◽  
S. X. SUN
Keyword(s):  
Neural Network ◽  
Wavelet Neural Network ◽  
Test Results ◽  
Factors Affecting ◽  
Price Forecast ◽  
Network Algorithm ◽  
Auction Price ◽  
Variable Weight ◽  
Fuzzy Neural ◽  
Neural Network Algorithm

As we all know, the factors affecting the price of equipment are more complicated, but these factors still have a great correlation. How can we accurately predict the price of equipment? Based on the study of the tight support and smoothness of wavelet function, this paper proposes a correlation variable weight wavelet neural network algorithm to predict the price of 162 devices. The test results show that if the weight is not reduced, the predicted price is 0, and the error is still large. However, by arranging the data from small to large, the variable weighted wavelet neural network algorithm is used to predict the result closer to the auction price, which overcomes the incompatibility of the algorithm iteration and provides a reference for accurately predicting the price of the device.

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