Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Naseem, Imran"'
Autor:
Hussain, Syed Saiq, Usman, Muhammad, Siddique, Taha Hasan Masood, Naseem, Imran, Togneri, Roberto, Bennamoun, Mohammed
In this research a novel stochastic gradient descent based learning approach for the radial basis function neural networks (RBFNN) is proposed. The proposed method is based on the q-gradient which is also known as Jackson derivative. In contrast to t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e558cdc6ea66ada5ba751e09a7cd9e91
Autor:
Ibrahim, Shakeel Muhammad, Ibrahim, Muhammad Sohail, Usman, Muhammad, Naseem, Imran, Moinuddin, Muhammad
Heart is one of the vital organs of human body. A minor dysfunction of heart even for a short time interval can be fatal, therefore, efficient monitoring of its physiological state is essential for the patients with cardiovascular diseases. In the re
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::42d6b0050b141f2fd040ea34b9e1ab21
Autor:
Sadiq, Alishba, Usman, Muhammad, Khan, Shujaat, Naseem, Imran, Moinuddin, Muhammad, Al-Saggaf, Ubaid M.
Channel estimation is an essential part of modern communication systems as it enhances the overall performance of the system. In recent past a variety of adaptive learning methods have been designed to enhance the robustness and convergence speed of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::79ea280a3da2abd21c312ffa775959ce
In this work, a new class of stochastic gradient algorithm is developed based on $q$-calculus. Unlike the existing $q$-LMS algorithm, the proposed approach fully utilizes the concept of $q$-calculus by incorporating time-varying $q$ parameter. The pr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c492e3c4165cd812f5a0ed95ec68dccc
The purpose of this note is to discuss some aspects of recently proposed fractional-order variants of complex least mean square (CLMS) and normalized least mean square (NLMS) algorithms in ``Design of Fractional-order Variants of Complex LMS and Norm
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d1439f1c4fe5b6d27e1d0df9e50306a8
The purpose of this paper is to indicate that the recently proposed Momentum fractional least mean squares (mFLMS) algorithm has some serious flaws in its design and analysis. Our apprehensions are based on the evidence we found in the derivation and
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::be4d570c911c59331c138c5d97f73366