We investigate the effectiveness of connectionist architectures for predicting the future behavior of nonlinear dynamical systems. We focus on real-world ...

We investigate the effectiveness of connectionist architectures for predicting the future behavior of nonlinear dynamical systems.

PREDICTING THE FUTURE: A CONNECTIONIST APPROACH. Andreas S. Weigend. Physics Department, Stanford University, Stanford, CA 94305, USA. Bernardo A. Huberman.

A hybrid approach, incorporating concepts of nonlinear dynamics in artificial neural networks (ANN), is proposed to model a time series generated by complex ...

PREDICTING THE FUTURE: A CONNECTIONIST APPROACH. Andreas S. Weigend(. Stanford U. ) ,. Bernardo A. Huberman(. Stanford U. ) ,. David E. Rumelhart(.

The ability to forecast the behavior of a system hinges on two types of knowledge. The first and most powerful one is the knowledge of the laws underlying a ...

15. AS Weigend, BA Huberman, DE Rumelhart. Predicting the future: a connectionist approach. International Journal of Neural Systems, 1 (3) (1990), pp. 193-209.

In this paper, we propose a Graph Neural Network (GNN)-based algorithm which exploits Virtual Network Function Forwarding Graph (VNF-FG) topology information to ...

Predicting the future: a connectionist approach. International Journal of Neural. Systems, 1, 193±209. Williams, R. J., & Zipser, D. (1989). A learning ...

Time series forecasting using NNs vs. B&J methodology. Simulations 1991. Weigend A, Huberman B,et al. Predicting the future; a connectionist approach. Intl J ...