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 …

Reliable and precise multi-step-ahead flood forecasts are crucial and beneficial to decision
makers for mitigating flooding risks. For a river basin undergoing fast urban development, its …

This paper presents fuzzy wavelet neural network (FWNN) models for prediction and
identification of nonlinear dynamical systems. The proposed FWNN models are obtained …

This paper presents a novel recurrent fuzzy neural network, called an interactively recurrent
self-evolving fuzzy neural network (IRSFNN), for prediction and identification of dynamic …

Local modeling approaches, owing to their ability to model different operating regimes of
nonlinear systems and processes by independent local models, seem appealing for …

In this study, a new hybrid forecasting method is proposed. The proposed method is called
autoregressive adaptive network fuzzy inference system (AR–ANFIS). AR–ANFIS can be …

This paper proposes an Externally Recurrent Neural Network (ERNN) for approximating the
unknown dynamics of complex nonlinear systems and time series prediction. The proposed …

In this paper, an efficient self-organizing recurrent radial basis function neural network
(RRBFNN), is developed for nonlinear system modeling. In RRBFNN, a two-steps learning …

Aim Fuzzy wavelet neural network (FWNN) has proven to be a promising strategy in the
identification of nonlinear systems. The network considers both global and local properties …