… nonlinear problem, and it faces new challenges in model establishment. Artificial neural
networks are widely used in predicting nonlinear time … in the neural network by passing errors. …

… directly by analytical approach because of its inherent nonlinearity. To tackle this difficulty,
an … It is worth emphasizing that only integer-order nonlinear systems are considered in the …

… are three models of the neural processors that make up feedforward neural networks (FNNs). …
In this paper, an enhanced marine predators algorithm (MPA) based on the ranking-based …

… , predator–prey models have been poorly studied with respect to the interaction of nonlinear
… insight that the problem with the neural network Lyapunov exponent estimates was in model …

… tree (DT), K-nearest neighbor, and neural network probability classifiers were compared
to identify normal and nonlinear techniques. One of the most useful nature-inspired optimization …

… neural networks (ANNs) to predict the number of cases of COVID-19 in Brazil and Mexico
in the upcoming days. Prey predator … Additionally, the nonlinear regression equations of the …

… predators algorithm (MPA) and adaptive neuro-fuzzy inference system (ANFIS) is presented
to identify nonlinear … The performance of the MPA is evaluated on eight nonlinear systems. …

… Several ML algorithms have also been applied in the modelling of nonlinear hydrologic
processes; for instance, artificial neural networks (ANN) [23], [24], support vector regression (SVR…

… This paper presents a neural networks simulation of two … a time delayed prey–predator
system incorporating a prey refuge. … In the learning of nonlinear dynamics, we use the following …

… between predictions of neural network models and experimental … neural network models.
In the final experiment, we test whether density, area, and/or number have effects on predator …