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 proposes a multivariate chaotic Extreme Learning Machine (ELM) model for the
prediction of the displacement of reservoir landslides. The displacement time series of the …

Due to the fluctuation and complexity of the tourism industry, it is difficult to capture its non-
stationary property and accurately describe its moving tendency. In this study, a novel …

Time series analysis and forecasting have become an active research area for a couple of
years in various domains like signal processing, weather forecasting, earthquake prediction …

Life prognostics are an important way to reduce production loss, save maintenance cost and
avoid fatal machine breakdowns. Predicting the remaining life of rolling bearing with small …

Landslide displacement system is generally characterized by non-stationary and nonlinear
characteristics. Traditionally, many artificial neural network (ANN) models have been …

Many nonlinear models have been proposed to forecast groundwater level. However, the
evidence of chaos in groundwater levels in landslide has not been explored. In addition …

In this paper, we study robust design of uncertain systems in a probabilistic setting by means
of linear quadratic regulators (LQR). We consider systems affected by random bounded …

State space reconstruction is the foundation of chaotic system modeling. Selection of
reconstructed variables is essential to the analysis and prediction of multivariate chaotic time …

This paper proposes a novel hybrid forecasting model known as GLSSVM, which combines
the group method of data handling (GMDH) and the least squares support vector machine …