39,240 research outputs found
Parameter estimation for VSI-Fed PMSM based on a dynamic PSO with learning strategies
© 1986-2012 IEEE.A dynamic particle swarm optimization with learning strategy (DPSO-LS) is proposed for key parameter estimation for permanent magnet synchronous machines (PMSMs), where the voltage-source inverter (VSI) nonlinearities are taken into account in the parameter estimation model and can be estimated simultaneously with other machine parameters. In the DPSO-LS algorithm, a novel movement modification equation with variable exploration vector is designed to effectively update particles, enabling swarms to cover large areas of search space with large probability and thus the global search ability is enhanced. Moreover, a Gaussian-distribution-based dynamic opposition-based learning strategy is developed to help the pBest jump out local optima. The proposed DPSO-LS can significantly enhance the estimator model accuracy and dynamic performance. Finally, the proposed algorithm is applied to multiple parameter estimation including the VSI nonlinearities of a PMSM. The performance of DPSO-LS is compared with several existing PSO algorithms, and the comparison results show that the proposed parameters estimation method has better performance in tracking the variation of machine parameters effectively and estimating the VSI nonlinearities under different operation conditions
Mutual Information Maximizing Quantum Generative Adversarial Network and Its Applications in Finance
One of the most promising applications in the era of NISQ (Noisy
Intermediate-Scale Quantum) computing is quantum machine learning. Quantum
machine learning offers significant quantum advantages over classical machine
learning across various domains. Specifically, generative adversarial networks
have been recognized for their potential utility in diverse fields such as
image generation, finance, and probability distribution modeling. However,
these networks necessitate solutions for inherent challenges like mode
collapse. In this study, we capitalize on the concept that the estimation of
mutual information between high-dimensional continuous random variables can be
achieved through gradient descent using neural networks. We introduce a novel
approach named InfoQGAN, which employs the Mutual Information Neural Estimator
(MINE) within the framework of quantum generative adversarial networks to
tackle the mode collapse issue. Furthermore, we elaborate on how this approach
can be applied to a financial scenario, specifically addressing the problem of
generating portfolio return distributions through dynamic asset allocation.
This illustrates the potential practical applicability of InfoQGAN in
real-world contexts.Comment: 15 pages, 15 figure
Strong Asymptotic Assertions for Discrete MDL in Regression and Classification
We study the properties of the MDL (or maximum penalized complexity)
estimator for Regression and Classification, where the underlying model class
is countable. We show in particular a finite bound on the Hellinger losses
under the only assumption that there is a "true" model contained in the class.
This implies almost sure convergence of the predictive distribution to the true
one at a fast rate. It corresponds to Solomonoff's central theorem of universal
induction, however with a bound that is exponentially larger.Comment: 6 two-column page
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