Prediction of multidimensional time-series data using a recurrent neural network (RNN) trained by real-time recurrent learning (RTRL), unbiased online recurrent optimization (UORO), least mean squares ...

This Hopfield Network Toolbox is mainly focused in Continuous Hopfield Networks (CHNs). This Toolbox is based on the work by Javier Yáñez, Pedro M. Talaván and Lucas García. The Continuous Hopfield ...

High-precision temperature control technology is currently more and more important in industrial thermal processing systems. In this paper, an RNN controller with integral-proportional-derivative (IPD ...

A special kind of recurrent neural networks (RNN), i.e., Zhang neural networks (ZNN), has recently been proposed for online time-varying problems solving. In this paper, we generalize and investigate ...

Network models based on recurrent neural networks (RNNs) of continuous-variable rate units have been extensively studied to characterize network dynamics underlying neural computations (4–9). Methods ...

1 Institute of Geophysics of the Czech Academy of Sciences, Prague, Czechia; 2 Department of Geosciences, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia; 3 Seismik s.r.o., ...