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Research on Adaptive Neural Network Control System Based on Nonlinear U-Model with Time-Varying Delay

Fengxia Xu, Yao Cheng, Hongliang Ren, Shili Wang
2014 Mathematical Problems in Engineering  
Based on the idea of discrete-time U-model with time-varying delay, the identification algorithm of adaptive neural network is given for the nonlinear model.  ...  U-model can approximate a large class of smooth nonlinear time-varying delay system to any accuracy by using time-varying delay parameters polynomial.  ...  adaptive neural network control method proposed, Adaptive control system structure diagram.  ... 
doi:10.1155/2014/420713 fatcat:3aymy4rvkzdvdiww3kccb2ek4a

A generalized procedure in designing recurrent neural network identification and control of time-varying-delayed nonlinear dynamic systems

Xueli Wu, Jianhua Zhang, Quanmin Zhu
2010 Neurocomputing  
A generalized procedure in designing recurrent neural network identification and control of time-varying-delayed nonlinear dynamic systems. Neurocomputing, 73 (7-9).  ...  neural network The nonlinear system (1) can be approximated by the following continuous-time delayed neural network: sgn * y t C t y t A t f y t B t f y t t S t L t e t diag e t t (3) where T TT y t y  ...  Further the newly structured neural network and its corresponding adaptive laws have embedded more practical factors for applications.  ... 
doi:10.1016/j.neucom.2009.12.002 fatcat:n4nbn6476bhjddr23nab55peu4

Nonlinear Time-Delay Suspension Adaptive Neural Network Active Control

Yue Zhu, Sihong Zhu
2014 Abstract and Applied Analysis  
Based on the time-delay nonlinear model, an adaptive neural network structure for magneto rheological active suspension is presented.  ...  By recognizing and training the adaptive neural network, the adaptive neural network active suspension controller is obtained.  ...  Then, an adaptive neural network structure for magneto rheological active suspension is presented according to the time-delay nonlinear model.  ... 
doi:10.1155/2014/765871 fatcat:ywnfdhxwdnepdae7zmeytgts3m

Adaptive inverse control of linear and nonlinear systems using dynamic neural networks

G.L. Plett
2003 IEEE Transactions on Neural Networks  
In this paper, we see adaptive control as a three-part adaptive-filtering problem. First, the dynamical system we wish to control is modeled using adaptive system-identification techniques.  ...  The techniques work to control minimum-phase or nonminimum-phase, linear or nonlinear, single-input-single-output (SISO) or multiple-input-multiple-ouput (MIMO), stable or stabilized systems.  ...  Optimal Solution for a Nonlinear Adaptive Filter In principle, a neural network can emulate a very general nonlinear function.  ... 
doi:10.1109/tnn.2003.809412 pmid:18238019 fatcat:cmjjz67ykrg5hev25quhuokuvq

Nonlinear Aeroelastic System Identification Based on Neural Network

Bo Zhang, Jinglong Han, Haiwei Yun, Xiaomao Chen
2018 Applied Sciences  
This paper focuses on the nonlinear aeroelastic system identification method based on an artificial neural network (ANN) that uses time-delay and feedback elements.  ...  A time-delay recurrent neural network (TDRNN) is employed and trained to predict the pitch angle of the system. The chirp and sine excitation signals are used to verify the identified system.  ...  Figure 1 . 1 Structure of the time-delay recurrent neural network (TDRNN). Figure 1 . 1 Structure of the time-delay recurrent neural network (TDRNN).  ... 
doi:10.3390/app8101916 fatcat:x5ido6valjf5fpfsm4hhc6jrf4

Table of contents

2007 IEEE Transactions on Neural Networks  
Mi 1532 Identification of Nonlinear Systems With Unknown Time Delay Based on Time-Delay Neural Networks ................. ...............................................................................  ...  Zhang 1364 Control and Estimation Delayed Standard Neural Network Models for Control Systems ....................................................... M.  ... 
doi:10.1109/tnn.2007.905492 fatcat:f3pz66wocragvb2iywiqnvzx5i

NONLINEAR AUTOREGRESSIVE MOVING AVERAGE-L2 MODEL BASED ADAPTIVE CONTROL OF NONLINEAR ARM NERVE SIMULATOR SYSTEM.pdf

Mustefa Jibril
2020 Figshare  
Comparison weremade among the neural network controller with NARMA-L2 model, neural network controller with NARMA-L2model system identification based predictive controller and neural network controller  ...  with NARMA-L2 model, neural network controller is designedwith NARMA-L2 model system identification based predictive controller and neural network controller is designedwith NARMA-L2 model based model  ...  For the identification section, you can teach a neural network to approximate the nonlinear function N.  ... 
doi:10.6084/m9.figshare.12235343 fatcat:vfit32i7erffjnogamm3m4d5kq

Recurrent Spiking Neural Networks the Third Generation in Identification of Systems

Nadia AdnanShiltagh
2014 International Journal of Computer Applications  
In this paper the modified identification method for nonlinear systems is proposed based on Recurrent Spiking Neural Networks (RSNN).  ...  The RSNN structure is tested for the identification of the nonlinear systems.  ...  CONCLUSION In this paper, the modified structure of Recurrent Spiking Neural Network is presented to identify the nonlinear system.  ... 
doi:10.5120/15319-3627 fatcat:djbd24auwfebrkzhn65aduckm4

2014 Index IEEE Transactions on Neural Networks and Learning Systems Vol. 25

2014 IEEE Transactions on Neural Networks and Learning Systems  
., +, TNNLS Jun. 2014 1033-1044 Adaptive Consensus Control for a Class of Nonlinear Multiagent Time-Delay Systems Using Neural Networks.  ...  ., +, TNNLS Jul. 2014 1229-1262 Adaptive Consensus Control for a Class of Nonlinear Multiagent Time- Delay Systems Using Neural Networks.  ...  The Field of Values of a Matrix and Neural Networks. Georgiou, G.M., TNNLS Sep. 2014  ... 
doi:10.1109/tnnls.2015.2396731 fatcat:ztnfcozrejhhfdwg7t2f5xlype

Nonlinear Autoregressive Moving Average-L2 Model Based Adaptive Control of Nonlinear Arm Nerve Simulator System

2020 Journal of Information Engineering and Applications  
This paper considers the trouble of the usage of approximate strategies for realizing the neural controllers for nonlinear SISO systems.  ...  In this paper, neural network controller is designed with NARMA-L2 model, neural network controller is designed with NARMA-L2 model system identification based predictive controller and neural network  ...  For the identification section, you can teach a neural network to approximate the nonlinear function N.  ... 
doi:10.7176/jiea/10-3-03 fatcat:sv5pefxe45hehmgbzyu4nqahp4

A Survey on Method of System Identification

Gao Weipeng, He Qiwei, Yan Zhengtao
2017 DEStech Transactions on Engineering and Technology Research  
Neural network has good nonlinear tracking ability, self-learning ability to adapt and parallel information processing ability, offers a new way for nonlinear system identification problem.  ...  In identification of nonlinear system, it can according to neural network of nonlinear static or dynamic system to identify the structure, using nonlinear approximation capability of neural network, to  ...  Identification of the fuzzy logic has the unique superiority, it can effectively identify complex and pathological structure; For large time delay, time varying and multiple input single output nonlinear  ... 
doi:10.12783/dtetr/mdm2016/4997 fatcat:gst6dioqybaijozyywm7bdfko4

Comparison of Four Neural Network Learning Methods Based on Genetic Algorithm for Non-linear Dynamic Systems Identification

Dr. Rafid Ahmed Khalil
2012 Al-Rafidain Engineering Journal  
Four different dynamic neural networks are used for identification of the same nonlinear dynamic system, using the genetic algorithm (GA) to train the Layer-Recurrent Network (LRN), Focused Time-Delay  ...  This paper addresses the problem of identification using dynamic neural networks (DNNs) based on genetic algorithm (GA) for nonlinear dynamic systems.  ...  [4] , introduced multilayer neural networks for identification and adaptive control of nonlinear systems.  ... 
doi:10.33899/rengj.2012.47165 fatcat:lmcsa2twuzf2jfszo7kj5litzm

Control design for arbitrary complex nonlinear discrete-time systems based on direct NNMRAC strategy

Shengquan Li, Juan Li, Jinhao Qiu, Hongli Ji, KongJun Zhu
2011 Journal of Process Control  
A novel scheme of neural network model reference adaptive control is proposed for arbitrary complex nonlinear discrete-time systems, i.e., non-minimum phase system, time-delay system and minimum phase  ...  structure and accelerate the convergence speed.  ...  Introduction The model reference adaptive control (MRAC) scheme which guarantees the global asymptotic stability for linear discrete-time system and nonlinear minimum phase system was proposed in Refs  ... 
doi:10.1016/j.jprocont.2010.10.010 fatcat:d5g5me4bg5boxjonr7crz7yr34

Delay nonlinear system predictive control on MPSO+DNN

Min Han, Jia Fan
2009 2009 IEEE International Conference on Systems, Man and Cybernetics  
This paper presents a novel dynamic neural network (DNN) predictive control strategy based on modified particle swarm optimization (PSO) for long time delay nonlinear process.  ...  The proposed dynamic NN structure could approximate to the actual system model and obtain the pure delay time exactly.  ...  Therefore, this paper proposes an adaptive dynamic feedforward neural network based on modified particle swarm optimization algorithm to the long time delay nonlinear system predictive control.  ... 
doi:10.1109/icsmc.2009.5346799 dblp:conf/smc/HanF09 fatcat:lnxh6cexarg75p5rsu3lskkr4a

Foundation of Notation and Classification of Nonconventional Static and Dynamic Neural Units

Ivo Bukovsky, Zeng-Guang Hou, Jiri Bila, Madan M. Gupta
2007 6th IEEE International Conference on Cognitive Informatics  
architecture and minimizing number of neural parameters; The paper introduces basic types of nonconventional the current research of time-delay dynamic neural units artificial neural units and focuses  ...  their notation and TmD-DNU and consequently of time-delay dynamic  ...  Then each of these attributes, the rather more suitable for standalone system identification dynamic order and the order of the nonlinearity (basically rather than for the network implementations. the  ... 
doi:10.1109/coginf.2007.4341916 dblp:conf/IEEEicci/BukovskyHBG07 fatcat:sgjdsrc7p5hj5kf7n2uksubsza
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