Nonlinear System Identification with Regularized Local FIR Model Networks

Nonlinear System Identification and Control Using Local Model Networks

Nolinear System Identification Based on Local Sub-Model Networks

Local sub-model networks based nonlinear approximated model and the identification algorithm are developed for nonlinear system. The structure of local sub-model is selected through a criterion with respect to both the approximation accuracy and model simplicity for the further applications. The computional load of identification does not change so much with the increaing of number of sub-model...

A Regularized Optimization Model for Community Identification in Complex Networks

Identifying community structure is an important step to reveal the functional characteristics of complex networks. Recently, many models and algorithms have been designed to identify communities in a given network. Here, we propose a general mathematical programming framework to optimize some modularity criteria under certain constraints. We then show that several existing models are special ca...

Nonlinear System Identification Using Spatiotemporal Neural Networks

The so-called spatiotemporal neural network is considered. This is a neural network where the conventional weight multiplication operation is replaced by a linear filtering operation [l]. A training algorithm is derived for such networks. The problem of nonlinear system identification is considered as an application for spatiotemporal networks. Nonlinear system identification is one of the chal...

Constructive state space model induced kernels for regularized system identification ?

There are two key issues for the kernel-based regularization method: the kernel structure design and the hyper-parameter estimation. In this contribution, we introduce a new family of kernel structures based on state space models. It has more flexible and more general structure, and includes some of stable spline kernels and diagonal correlated kernels as special cases. We also tested a differe...