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 …

This paper develops a hybrid method for nonlinear and chaotic time series forecasting
based on a local linear neuro-fuzzy model (LLNF) and optimized singular spectrum analysis …

This paper studies the prediction of the output voltage reduction caused by degradation
during nominal operating condition of a PEM fuel cell stack. It proposes a methodology …

Residual analysis using hybrid Elman–NARX neural network along with embedding
theorem is used to analyze and predict chaotic time series. Using embedding theorem, the …

In this paper, the problem of health monitoring and prognosis of aircraft gas turbine engines
is considered by using computationally intelligent methodologies. Two different dynamic …

As a powerful paradigm for knowledge representation and a simulation mechanism
applicable to numerous research and application fields, Fuzzy Cognitive Maps (FCMs) have …

Stock exchanges are considered major players in financial sectors of many countries. Most
Stockbrokers, who execute stock trade, use technical, fundamental or time series analysis in …

In this study, a new approach based on fuzzy cognitive map (FCM) and neuro-fuzzy
inference system (NFIS), called the neuro-fuzzy cognitive map (NFCM), is proposed. Here …

A self‐constructing fuzzy neural network (SCFNN) has been successfully used for chaotic
time series prediction in the literature. In this paper, we propose the strategy of adding a …

The complexity of hydrological processes and lack of data for modeling require the use of
specific tools for non-linear natural phenomenon. In this paper, an effort has been made to …