In this paper, we describe a stabilization method for linear time-delay systems which extends the classical pole placement method for ordinary di7erential equations. Unlike methods based on :nite spectrum assignment, our method does not render the closed loop system, :nite dimensional but consists of controlling the rightmost eigenvalues. Because these are moved to the left half plane in a (qua...

A novel method for determining the set of parameters for a phase space representation of a time series is proposed. Based upon the differential entropy, both the optimal embedding dimension , and time lag , are simultaneously determined. The choice of these parameters is closely related to the length of the optimal tap input delay line of an adaptive filter or time-delay neural network. The met...

[naeini1] one of the biggest organizational challenges in deployment of strategies is the time lag between the cause-and-effect relations according to a lag between indicators of strategic objectives. with adding the time factor, the strategic objectives at different times have a causal relationship with each other. the causal relationships between strategic objectives are essential. in this pa...

modelling and forecasting stock market is a challenging task for economists and engineers since it has a dynamic structure and nonlinear characteristic. this nonlinearity affects the efficiency of the price characteristics. using an artificial neural network (ann) is a proper way to model this nonlinearity and it has been used successfully in one-step-ahead and multi-step-ahead prediction of di...

End-stage renal disease (ESRD) patients exhibit an increased incidence of peptic ulcer disease. Helicobacter pylori plays a central role in the development of peptic ulcers. The effect of early H pylori eradication on the recurrence of complicated peptic ulcer disease in ESRD patients remains unclear. The aim of the present study was to explore whether early H pylori eradication therapy in ESRD...

This note is concerned with the problem of semi-globally stabilizing a linear system with an input delay and a constraint on the energy of its input. Under the condition of null controllability with vanishing energy, the parametric Lyapunov equation based L2 low gain feedback is adopted to solve the problem. The proposed approach is applied to the linearized model of the relative motion in the ...

Modelling and forecasting Stock market is a challenging task for economists and engineers since it has a dynamic structure and nonlinear characteristic. This nonlinearity affects the efficiency of the price characteristics. Using an Artificial Neural Network (ANN) is a proper way to model this nonlinearity and it has been used successfully in one-step-ahead and multi-step-ahead prediction of di...

Deep learning models are essential tools for mid- to long-term runoff prediction. However, the influence of input time lag and output lead on prediction results in deep has been less studied. Based 290 schemas, this study specified different lags by sliding windows predicted process RNN (Recurrent Neural Network), LSTM (Long–short-term Memory), GRU (Gated Recurrent Unit) at five hydrological st...

Learning to store information over extended time intervals by recurrent backpropagation takes a very long time, mostly because of insufficient, decaying error backflow. We briefly review Hochreiter's (1991) analysis of this problem, then address it by introducing a novel, efficient, gradient-based method called long short-term memory (LSTM). Truncating the gradient where this does not do harm, ...