Abstract Deep state space models (SSMs) are an actively researched model class for temporal developed in the deep learning community which have a close connection to classic SSMs. The use of SSMs as black-box identification can describe wide range dynamics due flexibility neural networks. Additionally, probabilistic nature allows uncertainty system be modelled. In this work SSM and its paramete...

Nonlinear system identification is a main topic of modern identification. A new method for nonlinear system identification is presented by using Quantum Genetic Algorithm (QGA).The problems of nonlinear system identification are cast as function optimization overprameter space,and the Quantum Genetic Algorithm is adopted to solve the optimization problem. Simulation experiments show that: compa...

This paper presents an original method that can be used for identifying time-varying nonlinear damping characteristics of a nonlinear oscillation system. The method developed involves the nonparametric identification, in which only the system responses, namely, displacement and velocity need to be known for the identification. However, themethod is concernedwith a Volterratype integral equation...

due to the fact that the error surface of adaptive infinite impulse response (iir) systems is generally nonlinear and multimodal, the conventional derivative based techniques fail when used in adaptive identification of such systems. in this case, global optimization techniques are required in order to avoid the local minima. harmony search (hs), a musical inspired metaheuristic, is a recently ...