# Enhanced q-Least Mean Square

arXiv: https://arxiv.org/abs/1801.00410 · HTML: https://mlsanity.com/paper/1801.00410/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jan 2018.

## Built on by (most cited first, 1 of 1)

- [q-RBFNN:A Quantum Calculus-based RBF Neural Network](https://mlsanity.com/paper/2106.01370/) (2021)

## Abstract (arXiv)

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 proposed enhanced q-LMS (Eq-LMS) algorithm utilizes a novel, parameterless concept of error-correlation energy and normalization of signal to ensure high convergence, stability and low steady-state error. The proposed algorithm automatically adapts the learning rate with respect to the error. For the evaluation purpose the system identification problem is considered. Extensive experiments show better performance of the proposed Eq-LMS algorithm compared to the standard q-LMS approach.