Object detection based on RGC mask R‐CNN

Intl J Robust & Nonlinear

Yang

2020

156

This article studies the identification problem of the nonlinear sandwich systems. For the sandwich system, because there are inner variables which cannot be measured in the information vector of the identification models, it is difficult to identify the nonlinear sandwich systems. In order to overcome the difficulty, an auxiliary model is built to predict the estimates of inner variables by means of the output of the auxiliary model. For the purpose of employing the real-time observed data, a cost function with dynamical data is constructed to capture on-line information of the nonlinear sandwich system. On this basis, an auxiliary model stochastic gradient identification approach is proposed based on the gradient optimization. Moreover, an auxiliary model multiinnovation stochastic gradient estimation method is developed, which tends to enhance estimation accuracy by introducing more observed data dynamically. The numerical simulation is provided and the simulation results show that the proposed auxiliary model identification method is effective for the nonlinear sandwich systems.

Section: Discussionmentioning

confidence: 99%

Auxiliary model multiinnovation stochastic gradient parameter estimation methods for nonlinear sandwich systems

Intl J Robust & Nonlinear

Yang

2020

156

“…The proposed 2S-LS-MIESG algorithm in this article can joint some mathematical strategies 59,60 to explore new identification methods of linear and nonlinear stochastic systems [61][62][63][64] and can applied to other fields. [65][66][67][68] The pseudo-code of implementing the 2S-LS-MIESG algorithm is shown in Algorithm 2. Compute the gain vector L(t) and the covariance matrix P(t) by (49)-(50).…”

Section: E(p T) = E(t) Y(p T)mentioning

confidence: 99%

Two‐stage recursive identification algorithms for a class of nonlinear time series models with colored noise

Intl J Robust & Nonlinear

Gan

et al. 2020

This article concentrates on the recursive identification algorithms for the exponential autoregressive model with moving average noise. Using the decomposition technique, we transform the original identification model into a linear and nonlinear subidentification model and derive a two-stage least squares (LS) extended stochastic gradient (ESG) algorithm. In order to improve the parameter estimation accuracy, we employ the multi-innovation identification theory and develop a two-stage LS multi-innovation ESG algorithm. A simulation example is provided to test the effectiveness of the proposed algorithms.

“…Equations (31) to (44) form the ML-ADE algorithm and the pseudocode of the ML-ADE algorithm is showed in Algorithm 2. The proposed approaches in the article can combine other mathematical tools [44][45][46][47][48] and statistical strategies [49][50][51][52][53] to study the performances of some parameter estimation algorithms of other linear stochastic systems and nonlinear stochastic systems with different structures and disturbance noises [54][55][56][57][58] and can be applied to literatures [59][60][61][62][63] such as article-making systems.…”

Section: The Ml-ade Algorithmmentioning

confidence: 99%

Maximum likelihood‐based adaptive differential evolution identification algorithm for multivariable systems in the state‐space form

Cui

Adaptive Control & Signal

et al. 2020

Parameter estimation plays an important role in the field of system control. This article is concerned with the parameter estimation methods for multivariable systems in the state-space form. For the sake of solving the identification complexity caused by a large number of parameters in multivariable systems, we decompose the original multivariable system into some subsystems containing fewer parameters and study identification algorithms to estimate the parameters of each subsystem. By taking the maximum likelihood criterion function as the fitness function of the differential evolution algorithm, we present a maximum likelihood-based differential evolution (ML-DE) algorithm for parameter estimation. To improve the parameter estimation accuracy, we introduce the adaptive mutation factor and the adaptive crossover factor into the ML-DE algorithm and propose a maximum likelihood-based adaptive differential evolution algorithm. The simulation study indicates the efficiency of the proposed algorithms.