Recurrent neurons (and in particular LSTM cells) demonstrated to be efficient when used as
basic blocks to build sequence to sequence architectures, which represent the state-of-the …

The prediction of chaotic time series is important for both science and technology. In recent
years, this type of prediction has improved significantly with the development of deep …

Recurrent neural networks have recently proved the state-of-the-art approach in forecasting
complex oscillatory time series on a multi-step horizon. Researchers in the field investigated …

A new method for identification of an optimal set of time lags based on non-uniform attractor
embedding from the observed non-linear time series is proposed in this paper. Simple …

The Levenberg–Marquardt learning algorithm is applied for training a multilayer perception
with three hidden layer each with ten neurons in order to carefully map the structure of …

This work presents a hybrid modeling approach to data-driven learning and representation
of unknown physical processes and closure parameterizations. These hybrid models are …

Chaotic time series data is extensively applied in financial stocks, climate monitoring, and
sea clutter, in which data fusion from various sources and multi-sensor information make …

A finite impulse response neural network, with tap delay lines after each neuron in hidden
layer, is used. Genetic algorithm with arithmetic decimal crossover and Roulette selection …

Chaotic dynamics are the paradigm of complex and unpredictable evolution due to their built-
in feature of amplifying arbitrarily small perturbations. The forecasting of these dynamics has …

This work explores annealed cooperative–competitive learning of multiple modules of
Mahalanobis normalized radial basis functions (NRBF) with applications to nonlinear …