scholarly journals Rollover Control in Heavy Vehicles via Recurrent High Order Neural Networks

10.5772/5545 ◽  
2008 ◽  
Author(s):  
Luis J. ◽  
Edgar N. ◽  
Reza Langari ◽  
Danial Shahmirzadi
Keyword(s):  
Neural Networks ◽  
High Order ◽  
Heavy Vehicles

Rollover Prediction and Control in Heavy Vehicles Via Recurrent High Order Neural Networks

2011 ◽  
Vol 17 (1) ◽  
pp. 95-107 ◽  
Author(s):  
Edgar N. Sanchez ◽  
Luis J. Ricalde ◽  
Reza Langari ◽  
Danial Shahmirzadi
Keyword(s):  
Neural Networks ◽  
High Order ◽  
Heavy Vehicles ◽  
Prediction And Control ◽  
Rollover Prediction ◽  
And Control

Decentralized adaptive tracking control for high-order interconnected stochastic nonlinear time-varying delay systems with stochastic input-to-state stable inverse dynamics by neural networks

2019 ◽  
Vol 41 (13) ◽  
pp. 3612-3625 ◽  
Author(s):  
Wang Qian ◽  
Wang Qiangde ◽  
Wei Chunling ◽  
Zhang Zhengqiang
Keyword(s):  
Neural Networks ◽  
Tracking Control ◽  
Large Scale ◽  
Inverse Dynamics ◽  
Small Neighborhood ◽  
High Order ◽  
Interconnected Systems ◽  
Delay Systems ◽  
Rbf Neural Networks ◽  
Stochastic Input

The paper solves the problem of a decentralized adaptive state-feedback neural tracking control for a class of stochastic nonlinear high-order interconnected systems. Under the assumptions that the inverse dynamics of the subsystems are stochastic input-to-state stable (SISS) and for the controller design, Radial basis function (RBF) neural networks (NN) are used to cope with the packaged unknown system dynamics and stochastic uncertainties. Besides, the appropriate Lyapunov-Krosovskii functions and parameters are constructed for a class of large-scale high-order stochastic nonlinear strong interconnected systems with inverse dynamics. It has been proved that the actual controller can be designed so as to guarantee that all the signals in the closed-loop systems remain semi-globally uniformly ultimately bounded, and the tracking errors eventually converge in the small neighborhood of origin. Simulation example has been proposed to show the effectiveness of our results.

Stability analysis of high order neural networks with proportional delays

2020 ◽  
Vol 372 ◽  
pp. 33-39 ◽  
Author(s):  
Wenqi Shen ◽  
Xian Zhang ◽  
Yantao Wang
Keyword(s):  
Neural Networks ◽  
Stability Analysis ◽  
High Order ◽  
Proportional Delays

scholarly journals LMI-Based Approach for Exponential Robust Stability of High-Order Hopfield Neural Networks with Time-Varying Delays

2012 ◽  
Vol 2012 ◽  
pp. 1-8 ◽  
Author(s):  
Yangfan Wang ◽  
Linshan Wang
Keyword(s):  
Neural Networks ◽  
Linear Matrix Inequality ◽  
Robust Stability ◽  
High Order ◽  
Hopfield Neural Networks ◽  
Linear Matrix ◽  
Time Varying ◽  
Matrix Inequality ◽  
Global Exponential Robust Stability

This paper studies the problems of global exponential robust stability of high-order hopfield neural networks with time-varying delays. By employing a new Lyapunov-Krasovskii functional and linear matrix inequality, some criteria of global exponential robust stability for the high-order neural networks are established, which are easily verifiable and have a wider adaptive.

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