Developing real-time wave-net models for non-linear time-varying experimental processes

Zakeri, Vahid; Naghavi, S. Vahid; Safavi, Afsaneh

doi:10.1016/j.compchemeng.2009.02.003

Cited by 14 publications

(17 citation statements)

References 5 publications

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“…In addition to, perpendicularity and locality of wavelet basis functions give wavelets such strong properties to be attractive in estimation [34]. Mallat [35] for the first time formulates a multi-resolution analysis (MRA) framework for wavelet.…”

Section: Waveletmentioning

confidence: 99%

New estimation methodologies for well logging problems via a combination of fuzzy Kalman filter and different smoothers

Soltani

Kordestani

Aghaee

2016

Journal of Petroleum Science and Engineering

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Section: Waveletmentioning

confidence: 99%

New estimation methodologies for well logging problems via a combination of fuzzy Kalman filter and different smoothers

Soltani

Kordestani

Aghaee

2016

Journal of Petroleum Science and Engineering

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“…In the online process, as proposed by [7], wherever an error is occurred, the locations of the wavelets which support the error, are determined and those wavelets are retrained using the new online data at the same resolution. If a large error still exists, one can either retrain the wavelets at a lower resolution or add wavelets of a higher resolution to the model to capture more details and high frequency components.…”

Section: Online 2 L Learning Algorithmmentioning

confidence: 99%

“…A wave-net is defined as a one hidden layer network whose basis functions are drawn from a family of wavelets [7]. In fact, by incorporating the basis functions at a particular resolution, namely resolution m=0, the approximation at the coarsest resolution is obtained:…”

Section: B the Wave-net Trainingmentioning

confidence: 99%

“…Considering the time varying nature of the traffic system, the use of a convenient modeling tool which can be fast and easily updated based on the continuous stream of the traffic data could effectively increase the model performance. Since wave-nets are datadriven structures [7], having their parameters determined by the data, they seem to be a very promising tool for the online modeling of the traffic system. Three different learning algorithms for wave-nets are discussed in [6].…”

Section: Introductionmentioning

confidence: 99%

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A novel online wave-net based model for motorway traffic modeling

Kharazmi

Safavi

2011

The 2nd International Conference on Control, Instrumentation and Automation

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In this paper, a novel method of wave-net modeling for motorway traffic system is proposed. Due to the highly nonlinear behavior of the traffic system, and the capabilities of wave-nets in multi-resolution analysis and nonlinear function approximation, the use of the wave-net as a modeling tool for traffic system seems to be very promising. We have also utilized the principle component analysis technique to process the inputs of the network, in order to reduce the network dimension while preserving its accuracy. Because of the time varying nature of the traffic system, the need of an algorithm for updating the model is vital in order to guarantee the performance of the system and the control strategies. Hence, in this work we have used online L2 learning algorithms to update the model. Simulation results, which are based on the real traffic data, demonstrate the increased efficiency of the online model compared to the offline model without update.

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“…Temperature control is necessary to maintain product quality and equipment safety [1][2][3]. However, most thermal processes are inherently nonlinear and exhibit uncertainties and time delays [4,5]. Consequently, an appropriate control design becomes a challenging task.…”

Section: Introductionmentioning

confidence: 99%

Adaptive PID controller based on Lyapunov function neural network for time delay temperature control

Aftab

Shafiq

2015

2015 IEEE 8th GCC Conference &Amp; Exhibition

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Temperature is an important control variable in industrial processes. In this paper, an adaptive PID control algorithm has been discussed to track the process temperature. The presented control algorithm employs Lyapunov function based artificial neural networks for online tuning of proportional, integral and derivative actions. This algorithm has been successfully tested on the laboratory temperature control process trainer. For comparative analysis, the results have been contrasted with the conventional PID scheme. The experimental findings show that improved and stable tracking is achieved with the proposed adaptive PID controller.

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Developing real-time wave-net models for non-linear time-varying experimental processes

Cited by 14 publications

References 5 publications

New estimation methodologies for well logging problems via a combination of fuzzy Kalman filter and different smoothers

New estimation methodologies for well logging problems via a combination of fuzzy Kalman filter and different smoothers

A novel online wave-net based model for motorway traffic modeling

Adaptive PID controller based on Lyapunov function neural network for time delay temperature control

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