The most promising methods for identifying a fuzzy model are data clustering, cluster merging and subsequent projection of the clusters on the input variable space. This article proposes to modify this procedure by adding a cluster rotation step, and a method for the direct calculation of the consequence parameters of the fuzzy linear model. These two additional steps make the model identificat...

Structural system identification using recursive methods has been a research direction of increasing interest in recent decades. The two prominent methods, including the Extended Kalman Filter (EKF) and the Particle Filter (PF), also known as the Sequential Monte Carlo (SMC), are advantageous in this field. In this study, the system identification of a shake table test of a 4-story steel struct...

Nonlinear system identification is becoming an important tool which can be used to improve control performance. This paper describes the application of adaptive neuro-fuzzy inference system (ANFIS) model for controlling a car. The vehicle must follow a predefined path by supervised learning. Back-propagation gradient descent method was performed to train the ANFIS system. The performance of the...

Polynomially structured low-rank approximation problems occur in • algebraic curve fitting, e.g., conic section fitting, • subspace clustering (generalized principal component analysis), and • nonlinear and parameter-varying system identification. The maximum likelihood estimation principle applied to these nonlinear models leads to nonconvex optimization problems and yields inconsistent estima...

Residual errors (deviations between measurements and system models) can be caused by several reasons: observation/process noise, nonlinear products, system transients, unmodelled dynamics, etc. The first two cannot be explained by linear models, the latter two can. Identification procedures can be stopped when the latter are not present in a reasonable size model. Therefore, we need to distingu...

Effective methods for feature and model structure selection are very important for data-driven modeling, data mining, and system identification tasks. This paper presents a new method for selecting important variables (regressors) in nonlinear (dynamic) models with mixed discrete (categorical, fuzzy) and continuous inputs and outputs. The proposed method applies fuzzy association rule mining. T...

Spacecraft periodic-disturbance rejection using a realistic spacecraft hardware simulator and its associated models is investigated. The effectiveness of the dipole-type disturbance rejection filter on the current realistic nonlinear rigid-body spacecraft model is validated. However, it is shown that the rejection filter is not robust to disturbance frequency uncertainty. Therefore, system iden...

In this article, a new approach based on blockoriented nonlinear models for modeling and identification of aircraft nonlinear dynamics has been proposed. Some of the block-oriented nonlinear models are considered as flexible structures which are suitable for the identification of widely applicable dynamic systems. These models are able to approximate a wide range of system dynamics. Flying vehi...

This article extends the tensor network Kalman filter to matrix outputs with an application in recursive identification of discrete-time nonlinear multiple-input-multiple-output (MIMO) Volterra systems. This extension completely supersedes previous work, where only l scalar outputs were considered. The Kalman tensor equations are modified to accommodate for matrix outputs and their implementati...