Time-Varying Matrix Square Roots Solving via Zhang Neural Network and Gradient Neural Network: Modeling, Verification and Comparison

2009 ◽  
pp. 11-20 ◽  
Author(s):  
Yunong Zhang ◽  
Yiwen Yang ◽  
Ning Tan
Keyword(s):  
Neural Network ◽  
Network Modeling ◽  
Neural Network Modeling ◽  
Time Varying ◽  
Square Roots ◽  
Zhang Neural Network ◽  
Gradient Neural Network

scholarly journals Vibration Reliability Analysis of Aeroengine Rotor Based on Intelligent Neural Network Modeling Framework

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Jia-Qi Liu ◽  
Yun-Wen Feng ◽  
Cheng Lu ◽  
Wei-Huang Pan ◽  
Da Teng
Keyword(s):  
Neural Network ◽  
Reliability Analysis ◽  
Short Form ◽  
Network Modeling ◽  
Neural Network Modeling ◽  
Time Varying ◽  
Rotor Vibration ◽  
Modeling Framework ◽  
Highly Nonlinear ◽  
Vibration Reliability

In order to improve the accuracy and calculation efficiency of aeroengine rotor vibration reliability analysis, a time-varying rotor vibration reliability analysis method under the aeroengine operating state is proposed. Aiming at the highly nonlinear and strong coupling of factors affecting the reliability of aeroengine rotor vibration, an intelligent neural network modeling framework (short form-INNMF) is proposed. The proposed method is based on DEA, with QAR information as the analysis data, and four factors including engine working state, fuel/oil working state, aircraft flight state, and external conditions are considered to analyse the rotor vibration reliability. INNMF is based on the artificial neural network (ANN) algorithm through improved particle swarm optimization (PSO) algorithm and Bayesian Regularization (BR) optimization. Through the analysis of the rotor vibration reliability of the B737-800 aircraft during a flight mission from Beijing to Urumqi, the time-varying rotor vibration reliability was obtained, which verified the effectiveness and feasibility of the method. The comparison of INNMF, random forest (RF), and ANN shows that INNMF improves analysis accuracy and calculation efficiency. The proposed method and framework can provide useful references for aeroengine rotor vibration analysis, special treatment, maintenance, and design.

Modeling of Distillation Tower Temperature Based on D-FNN

2011 ◽  
Vol 383-390 ◽  
pp. 1463-1469
Author(s):  
Shu Zhi Gao ◽  
Jing Yang ◽  
Jun Fan
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Learning Algorithm ◽  
Network Modeling ◽  
Temperature Control System ◽  
Neural Network Modeling ◽  
Time Varying ◽  
Distillation Process ◽  
Dynamic Learning ◽  
Fuzzy Neural

Distillation temperature control system is characteristics of nonlinear time-varying and we use dynamic fuzzy neural network to model the temperature of distillation. Firstly, we introduce the structure and algorithm of dynamic fuzzy neural network; Second, after data preprocessing of distillation process, we use dynamic Fuzzy neural network modeling the temperature of distillation. Dynamic fuzzy neural network adopt dynamic learning algorithm, and characteristic of approximation. The simulation results show the effect and accuracy of Dynamic fuzzy neural network model ing method.

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