Directivity Maximization of Difference Patterns for Monopulse Microstrip Patch Arrays with Sidelobe Constraints. [PDF]
Li W, Jiao YC, Zhang Y, Zhang L.
europepmc +1 more source
The Ergodic Linear-Quadratic Optimal Control Problems for Stochastic Mean-Field Systems with Periodic Coefficients [PDF]
Jiacheng Wu, Qi Zhang
openalex +1 more source
The proposed hybrid osprey‐salp swarm optimization algorithm addresses optimal power flow (OPF) problems in smart grids incorporating solar, hydro, and thermal generators. The algorithm is validated on Institute of Electrical and Electronics Engineers 30‐, 57‐, and 118‐bus test systems across five single and multiobjective OPF scenarios.
Mujtaba Ali +5 more
wiley +1 more source
Period-doubling cascade to chaos and optimal quadratic harvesting in a prey-predator-scavenger model using Crowley-Martin functional response. [PDF]
Manoharan R +4 more
europepmc +1 more source
The proposed framework operates as a continuous cycle: organizational data streams feed into predictive optimization, which generates energy efficiency targets. These targets are translated into behavioral directives through human resource management mechanisms.
Huang Juan, Aimi Binti Anuar
wiley +1 more source
Interval-aware optimal control of PMSG-based wind energy conversion systems via piecewise Chebyshev inclusion. [PDF]
Razmjooy N.
europepmc +1 more source
Local Polynomial Regression and Filtering for a Versatile Mesh‐Free PDE Solver
A high‐order, mesh‐free finite difference method for solving differential equations is presented. Both derivative approximation and scheme stabilisation is carried out by parametric or non‐parametric local polynomial regression, making the resulting numerical method accurate, simple and versatile. Numerous numerical benchmark tests are investigated for
Alberto M. Gambaruto
wiley +1 more source
Weighted cost emission dispatch optimization using GA-APO hybridization under priority sensitive scheduling for thermal power systems. [PDF]
Srinivas C +5 more
europepmc +1 more source
Machine Learning Approaches to Forecast the Realized Volatility of Crude Oil Prices
ABSTRACT This paper presents an evaluation of the accuracy of machine learning (ML) techniques in forecasting the realized volatility of West Texas Intermediate (WTI) crude oil prices. We compare several ML algorithms, including regularization, regression trees, random forests, and neural networks, to several heterogeneous autoregressive (HAR) models ...
Talha Omer +3 more
wiley +1 more source
Portfolio Optimization: A Neurodynamic Approach Based on Spiking Neural Networks. [PDF]
Khan AH, Mohammed AM, Li S.
europepmc +1 more source

