A New Multi-Channel Pilot Model Identification Method for Use in Assessment of Simulator Fidelity

2006 ◽  
Author(s):  
Frank Nieuwenhuizen ◽  
Peter Zaal ◽  
Max Mulder ◽  
Marinus van Paassen
Keyword(s):  
Model Identification ◽  
Identification Method ◽  
Pilot Model

Multimodal Pilot Model Identification in Real Flight

2009 ◽  
Author(s):  
Peter Zaal ◽  
Daan Pool ◽  
Max Mulder ◽  
Marinus van Paassen ◽  
Jan Mulder
Keyword(s):  
Model Identification ◽  
Pilot Model

scholarly journals T-S Fuzzy Systems Optimization Identification Based on FCM and PSO

2020 ◽  
Author(s):  
Yaxue Ren ◽  
Fucai Liu ◽  
Jingfeng Lv ◽  
Aiwen Meng ◽  
Yintang Wen
Keyword(s):  
Membership Function ◽  
Gaussian Function ◽  
Model Identification ◽  
Fuzzy Model ◽  
Pso Algorithm ◽  
Identification Accuracy ◽  
Fine Tuning ◽  
Recursive Least Squares ◽  
Identification Method ◽  
Two Parameters

Abstract The division of fuzzy space is very important in the identification of premise parameters and the Gaussian membership function is applied to the premise fuzzy set. However, the two parameters of Gaussian membership function, center and width, are not easy to be determined. In this paper, a novel T-S fuzzy model optimal identification method of optimizing two parameters of Gaussian function based on Fuzzy c-means (FCM) and particle swarm optimization (PSO) algorithm is presented. Firstly, we use FCM algorithm to determine the Gaussian center for rough adjustment. Then, under the condition that the center of Gaussian function is fixed, the PSO algorithm is used to optimize another adjustable parameter, the width of the Gaussian membership function, to achieve fine tuning, so as to complete the identification of prerequisite parameters of fuzzy model. In addition, the recursive least squares (RLS) algorithm is used to identify the conclusion parameters. Finally, the effectiveness of this method for T-S fuzzy model identification is verified by simulation examples, and the higher identification accuracy can be obtained by using the novel identification method described compared with other identification methods.

Identification of Complex System Based on Neural Network

2012 ◽  
Vol 433-440 ◽  
pp. 4342-4347
Author(s):  
Zhen Hai Dou ◽  
Ya Jing Wang
Keyword(s):  
Neural Network ◽  
Complex System ◽  
Mean Squared Error ◽  
Model Identification ◽  
Complex Model ◽  
System Model ◽  
Mathematics Model ◽  
Identification Method ◽  
Squared Error ◽  
Actual Output

In order to conquer the difficulty of building up the mathematics model of some complex system, model identification method based on neural network is put forward. By this method, according to actual sample datum, the complex model of crude oil heating furnace is identified at appropriate quantity of net layers and notes. The identification results show that output of model can basically consistent with the actual output and their mean squared error (MSE) almost is 0. Therefore, model identification method based on neural network is an effective method in complex system identification.

A Combined SVM/OB-based Wiener Model Identification Method

2012 ◽  
Vol 45 (16) ◽  
pp. 638-643 ◽  
Author(s):  
Juan C. Gómez ◽  
Enrique Baeyens
Keyword(s):  
Model Identification ◽  
Wiener Model ◽  
Identification Method
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