Modeling and prediction with a class of time delay dynamic neural networks

Becerikli, Yaşar; Oysal, Yusuf

doi:10.1016/j.asoc.2006.01.012

Cited by 28 publications

(18 citation statements)

References 22 publications

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“…In this section, we demonstrate the effectiveness of the proposed fuzzy rule based driven orthogonal approximation on examples studied in [8,[16][17][18]. We want to find the optimum parameter vector p which optimizes a quadratic performance index (PI):…”

Section: Simulation Resultsmentioning

confidence: 99%

Fuzzy rule-base driven orthogonal approximation

Alcı

2007

Neural Comput Applic

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In this study, orthogonal approximation concept is applied to fuzzy systems. We propose a new useful model adapted from the well-known Sugeno type fuzzy system. The proposed fuzzy model is a generalization of the zeroorder Sugeno fuzzy system model. Instead of linear functions in standard Sugeno model, we use nonlinear functions in the consequent part. The nonlinear functions are selected from a trigonometric orthogonal basis. Orthogonal function parameters are trained along with the Sugeno fuzzy system. The proposed model is demonstrated using three simulations-a nonlinear piecewise-continuous scalar function modeling and filtering, nonlinear dynamic system identification, and time series prediction. Finally some performance comparisons are carried out.

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Section: Simulation Resultsmentioning

confidence: 99%

Fuzzy rule-base driven orthogonal approximation

Alcı

2007

Neural Comput Applic

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“…Many methods of quality prediction with the techniques are proposed such as liner predicting model, generalized liner predicting model, memory-based predictor [4] and neural networks [5,6]. Recently, support vector machines proposed by Vapnik have become a key machine learning technique [7].…”

Section: Introductionmentioning

confidence: 99%

The Quality Prediction in Small-batch Producing Based on Weighted Least Squares Support Vector Regression

Zhang

Dai

2016

MATEC Web of Conferences

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Abstract.A novel quality prediction method with mobile time window is proposed for small-batch producing process based on weighted least squares support vector regression (LS-SVR). The design steps and learning algorithm are also addressed. In the method, weighted LS-SVR is taken as the intelligent kernel, with which the small-batch learning is solved well and the nearer sample is set a larger weight, while the farther is set the smaller weight in the history data. A typical machining process of cutting bearing outer race is carried out and the real measured data are used to contrast experiment. The experimental results demonstrate that the prediction accuracy of the weighted LS-SVR based model is only 20%-30% that of the standard LS-SVR based one in the same condition. It provides a better candidate for quality prediction of small-batch producing process.

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“…The design of bandwidth allocation for video traffic is a complex problem [2]. The artificial neural networks are commonly used for video traffic prediction [3][4][5][6][7] due to their main advantages. They are massively parallel, achieve high computation rates, employ adaptive topology and weights, are capable of modeling complex non-linear mappings etc.…”

Section: Introductionmentioning

confidence: 99%

Prediction Methods for MPEG-4 and H.264 Video Transmission

Pilka¹,

Oravec²

2011

Journal of Electrical Engineering

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Video services became a large part of internet network traffic. Therefore understanding of video coding standards and video traffic sources, such as video trace files is highly important. In this paper we concentrate on the basic characteristics of mpeg-4 and h.264 video coding standards. We describe the concept of the i , p and b frames in these standards since they are the main feature of every video trace file. Then we describe the content of the video trace files since the trace files are important for researchers to investigate network performance and understanding of network features. These are the important issues in terms of assuring the quality of service (QoS) in multimedia applications spread across the internet. Traffic prediction and bandwidth allocation are the crucial parts in terms of QoS. In this kind of applications, the artificial neural networks are vastly used. Therefore we illustrate the results of neural networks for video traffic prediction using both mpeg-4 and h.264 trace files.

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Modeling and prediction with a class of time delay dynamic neural networks

Cited by 28 publications

References 22 publications

Fuzzy rule-base driven orthogonal approximation

Fuzzy rule-base driven orthogonal approximation

The Quality Prediction in Small-batch Producing Based on Weighted Least Squares Support Vector Regression

Prediction Methods for MPEG-4 and H.264 Video Transmission

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