Continuous-time system identification with neural networks: Model structures and fitting criteria

Dynamic neural networks with hybrid structures for nonlinear system identification

Dynamic neural networks are often used for nonlinear system identification. This paper presents a novel series-parallel dynamic neural network structure which is suitable for nonlinear system identification. A theoretical proof is given showing that this type of dynamic neural network is able to approximate finite trajectories of nonlinear dynamical systems. Also, this neural network is trained...

Neural Networks and Continuous Time

The fields of neural computation and artificial neural networks have developed much in the last decades. Most of the works in these fields focus on implementing and/or learning discrete functions or behavior. However, technical, physical, and also cognitive processes evolve continuously in time. This cannot be described directly with standard architectures of artificial neural networks such as ...

Nonlinear System Identification for Predictive Control using Continuous Time Recurrent Neural Networks and Automatic Differentiation

In this paper, a continuous time recurrent neural network (CTRNN) is developed to be used in nonlinear model predictive control (NMPC) context. The neural network represented in a general nonlinear statespace form is used to predict the future dynamic behavior of the nonlinear process in real time. An efficient training algorithm for the proposed network is developed using automatic differentia...

Continuous-Time Recurrent Neural Networks

Continuous-Time Bilinear System Identification

The objective of this paper is to describe a new method for identification of a continuous-time multiinput and multi-output bilinear system. The approach is to make judicious use of the linear-model properties of the bilinear system when subjected to a constant input. Two steps are required in the identification process. The first step is to use a set of pulse responses resulting from a constan...