A NOVEL KERNEL LEAST MEAN SQUARE ALGORITHM BASED ON WEIGHTED GAUSSIAN KERNEL

A NOVEL KERNEL LEAST MEAN SQUARE ALGORITHM BASED ON WEIGHTED GAUSSIAN KERNEL. Masters thesis, King Fahd University of Petroleum and Minerals.

[img] PDF (MSc Thesis)
g201547450_Salman Rauf (MSc Thesis - Signed).pdf - Accepted Version
Restricted to Repository staff only until 27 October 2022.

Download (9MB)

Arabic Abstract

ةرظن ةحورطٔالا هذه يف تمدق دقلددع فلتخي دق .كتحورطٔال ريصق يفصو صخلم وه صخلم درجم كرابتعا يف عض ، صخلم ةباتك دنع مت ، كلذ ىلع ةوالع .اهتاقيبطتو اهحاجن ءارو ةنماكلا ةيرظنلاو (KLMS) SquareAlgorithm Mean Least Kernel ةيمزراوخ نع ةلماش ةماع متت WKLMS. ةيمزراوخ ىلع لوصحلل ةنوزوملا Gaus-sian ةاون مادختساب (KLMS) Square Mean Least Kernel Anovel ةيمزراوخ حارتقا متي (MSE). عبرملا ٔاطخلا طسوتم ءادٔا نيسحت ىلع نالمعي نزولا رايتخال نيرايعم حارتقا لالخ نم نازؤالا ديدحت يف لثمتملا يدحتلا ةجلاعم يئاقلتلا طابترالا ةفوفصمل ةلثامم نوكت نٔا بجي حيجرتلا ةفوفصم فوفص نٔا يناثلا رايعملا حضوي .ةيبيرجتلا جئاتنلا مادختساب لؤالا رايعملا قاقتشا ةيليلحتلاو ةيبيرجتلا بيلاسٔالا نم لك نم اهيلع لوصحلا مت يتلا جئاتنلا تبثت (RBF). RadialBasis ةلاد زكارمب قلعتي اميف ةلكشتملا تالخدملل ةيلبقتسملا ثحبلا تاهاجتا ضعب حارتقا مت ، اًريخٔا WKLMS. مادختساب ءادٔالا يف ريبك نسحت قيقحت نكمي هنٔ

English Abstract

In this thesis I have provided a comprehensive overview of Kernel Least Mean Square Algorithm (KLMS), the theory behind its success, and its applications. Moreover, a novel Kernel Least Mean Square (KLMS) algorithm is proposed using a weighted Gaus- sian Kernel to yield the WKLMS algorithm. The challenge of defi ning the weights is addressed by proposing two weight selection criteria which improve the Mean Square Error (MSE) performance. The fi rst criterion is derived by using empirical results. Second criterion shows that the rows of weighting matrix should be similar to eigenvec- tors orthogonal to the input auto-correlation matrix formed with respect to the Radial Basis Function (RBF) Centers. Results obtained from both empirical and analytical techniques prove that signifi cant performance improvement can be achieved by using the WKLMS. Lastly, some future research directions are proposed.

Item Type: Thesis (Masters)
Subjects: Engineering
Electrical
Department: College of Engineering and Physics > Electrical Engineering
Committee Advisor: Zerguine, Azzedine
Committee Members: Ghadban, Samir and Zidouri, Abdul Malik and Abu AlSaud, Wajih
Depositing User: Salman Rouf (g201547450)
Date Deposited: 28 Oct 2021 05:41
Last Modified: 28 Oct 2021 05:41
URI: https://eprints.kfupm.edu.sa/id/eprint/141969