scholarly journals Dynamics of fractional order nonlinear system: A realistic perception with neutrosophic fuzzy number and Allee effect

Author(s):  
Najeeb Alam Khan ◽  
Oyoon Abdul Razzaq ◽  
Fatima Riaz ◽  
Ali Ahmadian ◽  
Norazak Senu
2012 ◽  
Vol 70 (1) ◽  
pp. 475-479 ◽  
Author(s):  
Ling-Dong Zhao ◽  
Jian-Bing Hu ◽  
Jian-An Fang ◽  
Wen-Bing Zhang

2013 ◽  
Vol 62 (2) ◽  
pp. 020502
Author(s):  
Li Li-Xiang ◽  
Peng Hai-Peng ◽  
Luo Qun ◽  
Yang Yi-Xian ◽  
Liu Zhe

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yang Zheng ◽  
Jiayin Wu ◽  
Ming Xu

The random response and mean crossing rate of the fractional order nonlinear system with impact are investigated through the equivalent nonlinearization technique. The random additive excitation is Gaussian white noise, while the impact is described by a phenomenological model, which is developed from the actual impact process experiments. Based on the equivalent nonlinearization technique, one class of random nonlinear system with exact probability density function (PDF) solution of response is selected. The criterion of the appropriate equivalent nonlinear system is the similarity with the original system on the damping, stiffness, and inertia. The more similar, the higher the precision. The optimal unknown parameters of the equivalent random nonlinear system in the damping and stiffness terms are determined by the rule of smallest mean-square difference. In the view of equivalent nonlinearization technique, the response of the original system is the same as that of the equivalent system with the optimal unknown parameters in analytical solution manner. Then, the mean crossing rate is derived from stationary PDF. The consistence between the results from proposed technique and Monte Carlo simulation reveals the accuracy of the proposed analytical procedure.


Author(s):  
Gholamreza Nassajian ◽  
Saeed Balochian

In this paper, multi-model estimation and fault detection using neural network is proposed for an unknown time continuous fractional order nonlinear system. Fractional differentiation is considered based on Caputo concept and the fractional order is considered to be between 0 and 1. In order to estimate a time continuous fractional order nonlinear system with unknown term in its dynamic, single-layer and double-layer RBF neural network is used. First, a parallel-series neural network observer is designed for state estimation. Weights of the neural network are updated adaptively and updating laws are presented in fractional order form. Using Lyapunov method, it is proved that state estimation error and weight estimation error of the neural network are bounded. Parameters of the neural estimator converge to ideal parameters which satisfy excitation condition stability. Then, multi-model estimation structure of fractional order nonlinear systems is presented and its application in fault detection is investigated. Finally, simulation results are presented to show efficiency of the proposed method.


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