A recursive parameter estimation algorithm for modeling signals with multi-frequencies
L Xu, G Song - Circuits, Systems, and Signal Processing, 2020 - Springer
In this paper, we focus on the modeling problem of the multi-frequency signals which contain
many different frequency components. Based on the Newton search and the measured data …
many different frequency components. Based on the Newton search and the measured data …
A hybrid method for imputation of missing values using optimized fuzzy c-means with support vector regression and a genetic algorithm
IB Aydilek, A Arslan - Information Sciences, 2013 - Elsevier
Missing values in datasets should be extracted from the datasets or should be estimated
before they are used for classification, association rules or clustering in the preprocessing …
before they are used for classification, association rules or clustering in the preprocessing …
Missing data imputation using fuzzy-rough methods
Missing values exist in many generated datasets in science. Therefore, utilizing missing data
imputation methods is a common and important practice. These methods are a kind of …
imputation methods is a common and important practice. These methods are a kind of …
Missing value imputation using a novel grey based fuzzy c-means, mutual information based feature selection, and regression model
AM Sefidian, N Daneshpour - Expert Systems with Applications, 2019 - Elsevier
The presence of missing values in real-world data is not only a prevalent problem but also
an inevitable one. Therefore, missing values should be handled carefully before the mining …
an inevitable one. Therefore, missing values should be handled carefully before the mining …
Incomplete data management: a survey
Incomplete data accompanies our life processes and covers almost all fields of scientific
studies, as a result of delivery failure, no power of battery, accidental loss, etc. However, how …
studies, as a result of delivery failure, no power of battery, accidental loss, etc. However, how …
Imputations of missing values using a tracking-removed autoencoder trained with incomplete data
X Lai, X Wu, L Zhang, W Lu, C Zhong - Neurocomputing, 2019 - Elsevier
The presence of missing values in incomplete datasets increases the difficulty of data
mining. In this paper, we use the autoencoder (AE) to model the incomplete data for …
mining. In this paper, we use the autoencoder (AE) to model the incomplete data for …
Investigation on the data augmentation using machine learning algorithms in structural health monitoring information
Structural health monitoring system plays a vital role in smart management of civil
engineering. A lot of efforts have been motivated to improve data quality through mean …
engineering. A lot of efforts have been motivated to improve data quality through mean …
A novel opposition-based arithmetic optimization algorithm for parameter extraction of PEM fuel cell
The model-identification and parameter extraction are a well-defined method for modeling
and development purposes of a proton exchange membrane fuel cell (PEMFC) to improve …
and development purposes of a proton exchange membrane fuel cell (PEMFC) to improve …
Data imputation via evolutionary computation, clustering and a neural network
In this paper, two novel hybrid imputation methods involving particle swarm optimization
(PSO), evolving clustering method (ECM) and autoassociative extreme learning machine …
(PSO), evolving clustering method (ECM) and autoassociative extreme learning machine …
[图书][B] Causality, correlation and artificial intelligence for rational decision making
T Marwala - 2015 - books.google.com
Causality has been a subject of study for a long time. Often causality is confused with
correlation. Human intuition has evolved such that it has learned to identify causality through …
correlation. Human intuition has evolved such that it has learned to identify causality through …