The nonlinear characteristics are studied in a new 4D Hopfield neural network model with
two nonlinear synaptic weights in this paper. The synaptic function is modeled by …

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Evolutionary algorithms are heuristic methods that have yielded promising results for solving
nonlinear, nondifferentiable, and multi-modal optimization problems in the power systems …

This paper proposes an experimental analysis on the convergence of evolutionary
algorithms (EAs). The effect of introducing chaotic sequences instead of random ones during …

Chaos optimization algorithms as a novel method of global optimization have attracted much
attention, which were all based on Logistic map. However, we have noticed that the …

The complex dynamics analysis of fractional-order neural networks is a cutting-edge topic in
the field of neural network research. In this paper, a fractional-order Hopfield neural network …

The computation of an optimal operation point in power systems is a nonlinear optimization
problem in functional space, which is not easy to deal with precisely, even for small-scale …

Recently Chen and Aihara have demonstrated both experimentally and mathematically that
their chaotic simulated annealing (CSA) has better search ability for solving combinatorial …

Chaotic dynamics and its possible applications are considered from the viewpoint of
engineering. Various applications, even to consumer products such as household …

Backpropagation (BP) algorithm is one of the most basic learning algorithms in deep
learning. Although BP has been widely used, it still suffers from the problem of easily falling …