Fuzzy Neural Network-based Time Delay Prediction for Networked Control Systems

Han, Chang-Wook

doi:10.12785/amis/080151

Cited by 13 publications

(3 citation statements)

References 11 publications

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“…So even if time variables' internal formation pattern is unknown or hard to describe, neural network can also learn from the training samples and the form a stable function relationship. Based on this characteristic, Han [2], Kuo & Sheng-Feng [3] pointed out that neural network can be seen as a multivariable, nonlinear and nonparametric statistical model.…”

Section: Literature Referencesmentioning

confidence: 99%

Inflation Forecast Based on BP Neural Network Model

Wen

Tan

2014

AMR

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This paper analyzes inflation forecast based on BP neural network model. Firstly, it reviews some references about BP neural network and finds that it is a nonlinear adaptive data-driven model with induction ability and a wide range of function approximation ability so that BP neural network could be applied into forecast research. Secondly, it builds up the BP neural network model to predict CPI, selecting the four indicators, which are excess liquidity, exchange rates, inflation expectation and macro-economic leading index. Then it carries out empirical experiment and takes advantage of the monthly data of the above four indicators from March 2005 to December 2012 to forecast CPI. The results show that when prediction period is 3 months, the maximum absolute error between forecast value and real value is 0.0139, and the minimum absolute error is 0.0005. When prediction period is 6 months, the maximum absolute error is not more than 0.02. It proves that BP neural network model can predict coming CPI trend at least 6 months according to the existing data and it means it is suitable for the study of inflation forecast.

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Section: Literature Referencesmentioning

confidence: 99%

Inflation Forecast Based on BP Neural Network Model

Wen

Tan

2014

AMR

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“…For the network-induced time-delay compensation problem, many scholars have conducted a great deal of research work. Robust control [4,5], intelligent control [6,7], predictive control [8,9], pinning control [10][11][12] and other time-delay compensation methods have led to a lot of developments and applications.…”

Section: Introductionmentioning

confidence: 99%

Networked Control System Time-Delay Compensation Based on Time-Delay Prediction and Improved Implicit GPC

Tian¹,

Li²,

Wang³

et al. 2015

Algorithms

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Abstract:The random time delay in a networked control system can usually deteriorate the control performance and stability of the networked control system. In order to solve this problem, this paper puts forward a networked control system random time-delay compensation method based on time-delay prediction and improved implicit generalized predictive control (GPC). The least squares support vector machine is used to predict the future time delay of network. The parameters of the least squares support vector machine time-delay prediction model are difficult to determine, and the genetic algorithm is used for least squares support vector machine optimal prediction parameter optimization. Then, an improved implicit generalized predictive control method is adopted to compensate for the time delay. The simulation results show that the method in this paper has high prediction accuracy and a good compensation effect for the random time delay of the networked control system, has a small amount of on-line calculation and that the output response and control stability of the system are improved.

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“…If time varying delay exceed the sampling time then the performance of the system degraded. So a fuzzy neural network to predict the sampling period of NCS was proposed in [5]. The fuzzy neural model was applicable to predict the communication time delay.…”

mentioning

confidence: 99%

Predictive Controller Design for Networked Control System

2019

IJITEE

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Abstract: A networked control system where sensor ( ), controller ( ) and an actuator ( ) are interacting to the process model or system through a communication channel. The model reliability depends on time delay of data transmission from to , to , controller processing time and packet dropouts. In this proposed work, model based network system is designed to achieve the desired response of the controlled system by minimizing the network time delay and improves con-troller performance in term of stability. Time delay mainly occurs due to time sharing of the medium as well extra overload of signal coding and processing phase. The time delays could be steady, limited, or even random, depends upon protocol used in the network and hardware. The network predictive control tech-nique effectively works on random delay. The predictive controller is used to analyses the future output conditions of the system based on the previous values. The simulated result shows that if time delay increases, the response of the system degraded and therefore for improving it the correct choice of prediction and control horizon is needed. The designed networked control system based on predictive functional control is simulated using True Time tool on Matlab. The ob-tained outcomes from the proposed technique accomplishes that the technique is effective and feasible.

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Fuzzy Neural Network-based Time Delay Prediction for Networked Control Systems

Cited by 13 publications

References 11 publications

Inflation Forecast Based on BP Neural Network Model

Inflation Forecast Based on BP Neural Network Model

Networked Control System Time-Delay Compensation Based on Time-Delay Prediction and Improved Implicit GPC

Predictive Controller Design for Networked Control System

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