Metaheuristic algorithms are widely known as efficient solutions for solving problems of
optimization. These algorithms supply powerful instruments with significant engineering …

With the growth and development of power grids, optimal utilization of electric networks is
very important. Because of the high cost of construction and development of power …

Abstract Machine learning has become a powerful approach in practical applications, such
as decision making, sentiment analysis and ontology engineering. To improve the overall …

Traditional gated recurrent unit neural network (GRUNN) generally faces the challenges of
poor generalization ability and low training efficiency in performance degradation trend …

Heuristic and deterministic optimization methods are extensively applied for the training of
artificial neural networks. Both of these methods have their own advantages and …

Heuristic stochastic optimization techniques such as genetic algorithm perform global
search, but they suffer from the problem of slow convergence rate near global optimum. On …

The majority of loads in distribution systems are inductive, resulting in a poor power factor,
which causes power losses, voltage instability, voltage changes and system security issues …

Classical long short-term memory neural network (LSTMNN) generally faces the challenges
of poor generalization property and low training efficiency in state degradation trend …

Quantum Fourier transform (QFT) is an important part of many quantum algorithms.
However, there are few reports on quantum fractional Fourier transform (QFRFT). The main …

Many current computational models that aim to simulate cortical and hippocampal modules
of the brain depend on artificial neural networks. However, such classical or even deep …