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What is Covariance

Handbook of Research on Fireworks Algorithms and Swarm Intelligence
Measure of the joint variability of two random variables.
Published in Chapter:
EFWA as a Method of Optimizing Model Parameters: Example of an Expensive Function Evaluation
Daniel C. Lee (Simon Fraser University, Canada) and Katherine Manson (British Columbia Institute of Technology, Canada)
DOI: 10.4018/978-1-7998-1659-1.ch004
Abstract
The Fireworks Algorithm (EFWA) is studied as a method to optimize the noise covariance parameters in an induction motor system model to control the motor speed without a speed sensor. The authors considered a system that employs variable frequency drives (VFDs) and executes an extended Kalman filter (EKF) algorithm to estimate the motor speed based on other measured values. Multiple optimizations were run, and the authors found that the EFWA optimization provided, on average, better solutions than the Genetic Algorithm (GA) for a comparable number of parameter set trials. However, EFWA parameters need to be selected carefully; otherwise, EFWA's early performance advantage over GA can be lost.
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More Results
The Dynamic Connectedness Between Global Macroeconomic Risks and International Stock Markets: A Diagonal BEKK Approach
A measure of a directional relationship between two random variables. A positive covariance shows that those variables move together while a negative one indicates an inverse relationship.
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Modeling Time-Varying Co-Movements Between Major Cryptocurrencies and Foreign Exchange Markets
A measure of a directional relationship between two random variables. A positive covariance shows that those variables move together while a negative one indicates an inverse relationship.
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Using Geographic Information System to Infollow the Fertilizers Pollution Migration
The statistical tendency of two variables of the same type, attribute, name,and so on, to vary in ways that are related to each other. Positive covariance occurs when both variables tend to be above their respective means together, and negative covariance occurs if one variable tends to be above its mean when the other variable is below its mean.
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