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22 papers · ranked by Valyu relevance

01
AarXiv2023
EM Based p-norm-like Constraint RLS Algorithm for Sparse System Identification
Shuyang Jiang, Kung Yao
In this paper, the recursive least squares (RLS) algorithm is considered in the sparse system identification setting. The cost function of RLS algorithm is regularized by a p-norm-like (0≤p≤1) constraint of the estimated system parameters. In order to minimize the regularized cost function, we transform it into a…
82relevance
02
AarXiv2021
Multi-Layered Recursive Least Squares for Time-Varying System Identification
Mohammad Towliat, Zheng Guo, Leonard J. Cimini, Xiang‐Gen Xia + 1 more
'Aijun Song'] Abstract—Traditional recursive least square (RLS) adaptive filtering is widely used to estimate the impulse responses (IR) of an unknown system. Nevertheless, the RLS estimator shows poor performance when tracking rapidly time-varying systems. In this paper, we propose a multi-layered RLS (m-RLS)…
82relevance
03
PPubMed19 May 2017
Non-Invasive Fetal Monitoring: A Maternal Surface ECG Electrode Placement-Based Novel Approach for Optimization of Adaptive Filter Control Parameters Using the LMS and RLS Algorithms
Radek Martinek, Radana Kahankova, Homer Nazeran, Jaromir Konecny + 7 more
This paper is focused on the design, implementation and verification of a novel method for the optimization of the control parameters (such as step size $μ$ and filter order N) of LMS and RLS adaptive filters used for noninvasive fetal monitoring. The optimization algorithm is driven by considering the ECG electrode…
81relevance
04
PPubMed21 Jul 2022
Early Stopping Criterion for Recursive Least Squares Training of Behavioural Models
Méabh Loughman, Sinéad Barton, Ronan Farrell, John Dooley
The necessity of the rapid evolution of wireless communications, with continuously increasing demands for higher data rates and capacity Zheng (Big datadriven optimization for mobile networks toward 5g 30:44-51, 2016), is constantly augmenting the complexity of radio frequency (RF) transceiver architecture. A…
81relevance
05
PPubMed13 Aug 2025
Robust Data-Reuse Regularized Recursive Least-Squares Algorithms for System Identification Applications †
Radu-Andrei Otopeleanu, Constantin Paleologu, Jacob Benesty, Laura-Maria Dogariu + 3 more
'Laura-Maria Dogariu' 'Cristian-Lucian Stanciu' 'Silviu Ciochină' 'Ka-Fai Cedric Yiu'] The recursive least-squares (RLS) algorithm stands out as an appealing choice in adaptive filtering applications related to system identification problems. This algorithm is able to provide a fast convergence rate for various types…
81relevance
06
PPubMed9 Mar 2022
Retrospection of Nonlinear Adaptive Algorithm-Based Intelligent Plane Image Interaction System
Zixi Guan, Raja Varma Pamba, Bhuvaneswari Balachander, Deepak Kumar Khare + 2 more
'Deepak Kumar Khare' 'Nabamita Deb' 'Rajasekhar Boddu'] This paper introduces the application and classification of an adaptive filtering algorithm in the image enhancement algorithm. And the filtering noise reduction impact is compared using MATLAB software for programming, image processing, LMS algorithm, RLS…
81relevance
07
BbioRxiv27 Sep 2022
A scalable implementation of the recursive least-squares algorithm for training spiking neural networks
Benjamin J. Arthur, Christopher M. Kim, Susu Chen, Stephan Preibisch + 1 more
Training spiking recurrent neural networks on neuronal recordings or behavioral tasks has become a prominent tool to study computations in the brain. With an increasing size and complexity of neural recordings, there is a need for fast algorithms that can scale to large datasets. We present optimized CPU and GPU…
81relevance
08
AarXiv2016
Reinforcement learning based local search for grouping problems: A case study on graph coloring
Yangming Zhou, Jin‐Kao Hao, Béatrice Duval
Grouping problems aim to partition a set of items into multiple mutually disjoint subsets according to some specific criterion and constraints. Grouping problems cover a large class of important combinatorial optimization problems that are generally computationally difficult. In this paper, we propose a general…
80relevance
09
PPubMed2 May 2012
Wrapper-based selection of genetic features in genome-wide association studies through fast matrix operations
Tapio Pahikkala, Sebastian Okser, Antti Airola, Tapio Salakoski + 1 more
'Tero Aittokallio'] Background Through the wealth of information contained within them, genome-wide association studies (GWAS) have the potential to provide researchers with a systematic means of associating genetic variants with a wide variety of disease phenotypes. Due to the limitations of approaches that have…
79relevance
10
AarXiv24 Jul 2026
On the Runtime Analysis of Reinforcement Learning Hyper-Heuristics
Pietro S. Oliveto, Zhenyu Wang, Peizhou Wu, Mengqing Xu
Selection Hyper-heuristics (HHs) automate algorithmic design by selecting from a set of low-level heuristics which one to apply at each stage of the optimisation process. Several impressive results have been recently rigorously proven regarding the performance of selection hyper-heuristics (HHs) for standard benchmark…
78relevance
11
BbioRxiv27 Apr 2020
Improved estimation of hemodynamic response in fNIRS using protocol constraint and wavelet transform decomposition based adaptive algorithm
Sahar Jahani, Seyed Kamaledin Setarehdan
Near infrared spectroscopy allows monitoring of oxy and deoxyhemoglobin concentration changes associated with hemodynamic response function (HRF). HRF is mainly affected by physiological interferences which occur in the superficial layers of the head. This makes HRF extracting a very challenging task. Recent studies…
76relevance
12
AarXivUndated
2602.05675
Authors not listed
—Over the past two decades, research in evolutionary multi-objective optimization has predominantly focused on continuous domains, with comparatively limited attention given to multi-objective combinatorial optimization problems (MOCOPs). Combinatorial problems differ significantly from continuous ones in terms of…
72relevance
13
BbioRxiv15 Mar 2019
Simple Framework for Constructing Functional Spiking Recurrent Neural Networks
Robert Kim, Yinghao Li, Terrence J. Sejnowski
Cortical microcircuits exhibit complex recurrent architectures that possess dynamically rich properties. The neurons that make up these microcircuits communicate mainly via discrete spikes, and it is not clear how spikes give rise to dynamics that can be used to perform computationally challenging tasks. In contrast…
66relevance
14
CchemRxiv21 Mar 2023
Reinforcement Learning in Crystal Structure Prediction
Elena Zamaraeva, Christopher M. Collins, Dmytro Antypov, Vladimir V. Gusev + 6 more
Crystal Structure Prediction (CSP) is a fundamental computational problem in materials science. Basin-hopping is a prominent CSP method that combines global Monte Carlo sampling to search over candidate trial structures with local energy minimisation of these candidates. The sampling uses a stochastic policy to…
65relevance
15
CchemRxiv21 Oct 2025
Artificial Intelligence and Olfaction: A Survey on the Sense of Smell for Robotics
Authors not listed
Machine olfaction—the artificial replication of the sense of smell—faces significant challenges due to the absence of large, standardized training datasets. Unlike vision, language, and audio models, which benefit from extensive corpora such as ImageNet, GLUE, and AudioSet, olfaction lacks scaled equivalents and…
64relevance
16
CchemRxiv20 Apr 2022
Sella, an open-source automation-friendly molecular saddle point optimizer
Eric Hermes, Khachik Sargsyan, Habib Najm, Judit Zádor
We present a new algorithm for the optimization of molecular structures to saddle points on the potential energy surface using a redundant internal coordinate system. This algorithm automates the procedure of defining the internal coordinate system, including the handling of linear bending angles, e.g. through the…
63relevance
17
PPubMed15 Dec 2023
MLBRSA: Multi-Learning-Based Reptile Search Algorithm for Global Optimization and Software Requirement Prioritization Problems
Jeyaganesh Kumar Kailasam, Rajkumar Nalliah, Saravanakumar Nallagoundanpalayam Muthusamy, Premkumar Manoharan + 1 more
'Saravanakumar Nallagoundanpalayam Muthusamy' 'Premkumar Manoharan' 'Heming Jia'] In the realm of computational problem-solving, the search for efficient algorithms tailored for real-world engineering challenges and software requirement prioritization is relentless. This paper introduces the Multi-Learning-Based…
63relevance
18
BbioRxiv10 May 2022
Deep Reinforcement Learning for Optimal Experimental Design in Biology
Neythen J. Treloar, Nathan Braniff, Brian Ingalls, Chris P. Barnes
The field of optimal experimental design uses mathematical techniques to determine experiments that are maximally informative from a given experimental setup. Here we apply a technique from artificial intelligence—reinforcement learning—to the optimal experimental design task of maximizing confidence in estimates of…
62relevance
19
CchemRxiv8 Aug 2023
Sample Efficient Reinforcement Learning with Active Learning for Molecular Design
Michael Dodds, Jeff Guo, Thomas Löhr, Alessandro Tibo + 2 more
Reinforcement learning (RL) is a powerful and flexible paradigm for searching for solutions in high-dimensional action spaces. However, bridging the gap between playing computer games with thousands of simulated episodes and solving real scientific problems with complex and involved environments (up to actual…
62relevance
20
CchemRxiv4 Apr 2024
Optimizing the Prediction of Adsorption in Metal-Organic Frameworks Leveraging Q-Learning
Etinosa Osaro, Yamil Colón
The application of machine learning (ML) techniques in materials science has revolutionized the pace and scope of materials research and design. In the case of metal-organic frameworks (MOFs), a promising class of materials due to their tunable properties and versatile applications in gas adsorption and separation, ML…
58relevance
21
BbioRxiv19 Jul 2019
PECLIDES Neuro A Personalisable Clinical Decision Support System for Neurological Diseases
Tamara Müller, Pietro Lio’
Neurodegenerative diseases such as Alzheimer’s and Parkinson’s impact millions of people worldwide. Early diagnosis has proven to greatly increase the chances of slowing down the diseases’ progression. Correct diagnosis often relies on the analysis of large amounts of patient data, and thus lends itself well to support…
53relevance
22
CchemRxiv10 Dec 2025
Opportunities and Challenges of Machine Learning in Atomic Layer Deposition
Authors not listed
The integration of machine learning methods is transforming many areas of research by, for instance, accelerating molecular dynamics simulations and enabling improved prediction and optimization of chemical reactions. However, despite this progress, the adoption of data-driven approaches in atomic layer deposition…
52relevance

Results for RLS algorithm

EM Based p-norm-like Constraint RLS Algorithm for Sparse System Identification

Multi-Layered Recursive Least Squares for Time-Varying System Identification

Non-Invasive Fetal Monitoring: A Maternal Surface ECG Electrode Placement-Based Novel Approach for Optimization of Adaptive Filter Control Parameters Using the LMS and RLS Algorithms

Early Stopping Criterion for Recursive Least Squares Training of Behavioural Models

Robust Data-Reuse Regularized Recursive Least-Squares Algorithms for System Identification Applications †

Retrospection of Nonlinear Adaptive Algorithm-Based Intelligent Plane Image Interaction System

A scalable implementation of the recursive least-squares algorithm for training spiking neural networks

Reinforcement learning based local search for grouping problems: A case study on graph coloring

Wrapper-based selection of genetic features in genome-wide association studies through fast matrix operations

On the Runtime Analysis of Reinforcement Learning Hyper-Heuristics

Improved estimation of hemodynamic response in fNIRS using protocol constraint and wavelet transform decomposition based adaptive algorithm

2602.05675

Simple Framework for Constructing Functional Spiking Recurrent Neural Networks

Reinforcement Learning in Crystal Structure Prediction

Artificial Intelligence and Olfaction: A Survey on the Sense of Smell for Robotics

Sella, an open-source automation-friendly molecular saddle point optimizer

MLBRSA: Multi-Learning-Based Reptile Search Algorithm for Global Optimization and Software Requirement Prioritization Problems

Deep Reinforcement Learning for Optimal Experimental Design in Biology

Sample Efficient Reinforcement Learning with Active Learning for Molecular Design

Optimizing the Prediction of Adsorption in Metal-Organic Frameworks Leveraging Q-Learning

PECLIDES Neuro A Personalisable Clinical Decision Support System for Neurological Diseases

Opportunities and Challenges of Machine Learning in Atomic Layer Deposition

EM Based p-norm-like Constraint RLS Algorithm for Sparse System Identification

Multi-Layered Recursive Least Squares for Time-Varying System Identification

Non-Invasive Fetal Monitoring: A Maternal Surface ECG Electrode Placement-Based Novel Approach for Optimization of Adaptive Filter Control Parameters Using the LMS and RLS Algorithms

Early Stopping Criterion for Recursive Least Squares Training of Behavioural Models

Robust Data-Reuse Regularized Recursive Least-Squares Algorithms for System Identification Applications †

Retrospection of Nonlinear Adaptive Algorithm-Based Intelligent Plane Image Interaction System

A scalable implementation of the recursive least-squares algorithm for training spiking neural networks

Reinforcement learning based local search for grouping problems: A case study on graph coloring

Wrapper-based selection of genetic features in genome-wide association studies through fast matrix operations

On the Runtime Analysis of Reinforcement Learning Hyper-Heuristics

Improved estimation of hemodynamic response in fNIRS using protocol constraint and wavelet transform decomposition based adaptive algorithm

2602.05675

Simple Framework for Constructing Functional Spiking Recurrent Neural Networks

Reinforcement Learning in Crystal Structure Prediction

Artificial Intelligence and Olfaction: A Survey on the Sense of Smell for Robotics

Sella, an open-source automation-friendly molecular saddle point optimizer

MLBRSA: Multi-Learning-Based Reptile Search Algorithm for Global Optimization and Software Requirement Prioritization Problems

Deep Reinforcement Learning for Optimal Experimental Design in Biology

Sample Efficient Reinforcement Learning with Active Learning for Molecular Design

Optimizing the Prediction of Adsorption in Metal-Organic Frameworks Leveraging Q-Learning

PECLIDES Neuro A Personalisable Clinical Decision Support System for Neurological Diseases

Opportunities and Challenges of Machine Learning in Atomic Layer Deposition