Adaptive bipartite consensus of competitive linear multi‐agent systems with asynchronous intermittent communication

The nonlinear autoregressive exogenous (NARX) model shows a strong expression capacity for nonlinear systems since these systems have limited information about their structures. However, it is difficult to model the NARX system with a relatively high dimension by using the popular polynomial NARX and the neural network efficiently. This article uses the tensor network B-spline (TNBS) to model the NARX system, whose representation of the multivariate B-spline weight tensor can alleviate the computation and store burden for processing high-dimensional systems. Furthermore, applying the multi-innovation identification theory and the hierarchical identification principle, the recursive algorithm by combining the l 2 -norm is proposed to the NARX system with Gaussian noise. Because of the local adjustability of the B-spline curve, the TNBS can fit nonlinear systems with strong nonlinearity by the meaning of setting a proper degree and knots number. Finally, a numerical experiment is given to demonstrate the effectiveness of the proposed algorithm.

Section: Discussionmentioning

confidence: 99%

Modeling nonlinear systems using the tensor network B‐spline and the multi‐innovation identification theory

Wang

Tang

Deng

2022

“…The proposed approaches in the article can combine some mathematical tools and identification methods [49][50][51][52][53][54][55][56] to study the parameter estimation issues of other linear stochastic systems and nonlinear stochastic systems with different structures and disturbance noises [57][58][59][60][61][62][63] and can be applied to literatures [64][65][66][67][68][69][70][71] such as paper-making engineering systems and so on.…”

Section: The Auxiliary Model-based Forgetting Factor Recursive Least ...mentioning

confidence: 99%

Auxiliary model‐based recursive least squares algorithm for two‐input single‐output Hammerstein output‐error moving average systems by using the hierarchical identification principle

Liu

2022

This article considers the parameter estimation problems of two‐input single‐output Hammerstein output‐error moving average systems. The system is decomposed into two subsystems based on the hierarchical principle. The first model is used to identify the linear parameters and the parameters of the unknown measurable information vector. The second model is for identifying non‐linear parameters. By using the auxiliary model, we introduce a forgetting factor to improve the parameter estimation accuracy. The auxiliary model‐based forgetting factor recursive least squares algorithm and the auxiliary model‐based forgetting factor multi‐innovation recursive least squares algorithm are presented. The simulation results indicate that the proposed algorithms are effective.

“…The proposed approaches in the article can combine some mathematical tools and identification methods [62][63][64][65][66][67][68][69] to study the parameter estimation issues of other linear stochastic systems and nonlinear stochastic systems with different structures and disturbance noises [70][71][72][73][74][75][76] and can be applied to literatures [77][78][79][80][81][82][83][84] such as paper-making systems, information processing, engineering systems and so on. The pseudo-code of implementing the BSO-GCMPN-GI algorithm is shown in Algorithm 1.…”

Section: Generalized Continuous Mixed P-norm Gradient Iterative Algor...mentioning

confidence: 99%

Generalized continuous mixed p‐norm based sliding window algorithm for a bilinear system with impulsive noise

Liu

Xiong

2022

This article investigates the identification issue of the bilinear system in the presence of the impulsive noise. The bilinear system based on the observer canonical form is translated into a regressive form, and a bilinear state observer is established to estimate the state variables. To overcome the effects of the impulsive noise to parameter estimation, the proposed algorithms employ a generalized continuous mixed p$$ p $$‐norm cost function, which can generate an adjustable gain that control the proportions of the error norms without resorting to a priori knowledge of the noise. Moreover, a sliding window is designed to update the dynamical data by removing the oldest data and adding the newest measurement data. An numerical example exhibits that the proposed algorithms can reduce the impact of the impulsive noise to parameter estimation and improve the parameter estimation accuracy compared with the conventional algorithms.