Results 41 to 50 of about 107,979 (335)
Plant identification via adaptive combination of transversal filters [PDF]
, 2006 For least mean-square (LMS) algorithm applications, it is important to improve the speed of convergence vs the residual error trade-off imposed by the selection of a certain value for the step size.
Arenas García, Jerónimo +3 more
core +2 more sources
RLS with Optimum Multiple Adaptive Forgetting Factors for SoC and SoH Estimation of Li-Ion Battery [PDF]
, 2017 Recursive least square (RLS) with a single forgetting factor has been commonly used for parameter and state estimation of dynamical systems. In many applications such as robotics, electric vehicles, renewable energy systems, and smart-grid, accurate ...
Kanarachos, Stratis +3 more
core +1 more source
Advanced Intelligent Discovery, EarlyView.Feature selection combined with machine learning and high‐throughput experimentation enables efficient handling of high‐dimensional datasets in emerging photovoltaics. This approach accelerates material discovery, improves process optimization, and strengthens stability prediction, while overcoming challenges in data quality and model scalability to ...
Jiyun Zhang +5 more
wiley +1 more source
Recursive Minimum Complex Kernel Risk-Sensitive Loss Algorithm
Entropy, 2018 The maximum complex correntropy criterion (MCCC) has been extended to complex domain for dealing with complex-valued data in the presence of impulsive noise.
Guobing Qian, Dan Luo, Shiyuan Wang
doaj +1 more source
Large-scale wave-front reconstruction for adaptive optics systems by use of a recursive filtering algorithm [PDF]
, 2005 We propose a new recursive filtering algorithm for wave-front reconstruction in a large-scale adaptive optics system. An embedding step is used in this recursive filtering algorithm to permit fast methods to be used for wave-front reconstruction on an ...
Britton, Matthew +2 more
core +1 more source
Advanced Intelligent Discovery, EarlyView.Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley +1 more source
Hysteresis Modeling of Magnetic Shape Memory Alloy Actuator Based on Krasnosel'skii-Pokrovskii Model
The Scientific World Journal, 2013 As a new type of intelligent material, magnetically shape memory alloy (MSMA) has a good performance in its applications in the actuator manufacturing.
Miaolei Zhou, Shoubin Wang, Wei Gao
doaj +1 more source
A simplified fractional order impedance model and parameter identification method for lithium-ion batteries. [PDF]
PLoS ONE, 2017 Identification of internal parameters of lithium-ion batteries is a useful tool to evaluate battery performance, and requires an effective model and algorithm. Based on the least square genetic algorithm, a simplified fractional order impedance model for
Qingxia Yang +3 more
doaj +1 more source
An Online Parallel and Distributed Algorithm for Recursive Estimation of Sparse Signals [PDF]
, 2015 In this paper, we consider a recursive estimation problem for linear regression where the signal to be estimated admits a sparse representation and measurement samples are only sequentially available.
Palomar, Daniel P. +3 more
core +1 more source
Advanced Intelligent Discovery, EarlyView.Machine learning predicts activation energies for key steps in the water‐gas shift reaction on 92 MXenes. Random Forest is identified as the most accurate model. Reaction energy and reactant LogP emerge as key descriptors. The approach provides a predictive framework for catalyst design, grounded in density functional theory data and validated through ...
Kais Iben Nassar +3 more
wiley +1 more source