Short-term prediction method of blood glucose based on temporal multi-head attention mechanism for diabetic patients

Intl J Robust & Nonlinear

Xiao

et al. 2023

114

For equation-error autoregressive moving average systems, that is, Box-Jenkins systems, this paper presents a filtered auxiliary model generalized extended stochastic gradient identification method, a filtered auxiliary model multi-innovation generalized extended stochastic gradient identification method, a filtered auxiliary model recursive generalized extended gradient identification method, a filtered auxiliary model multi-innovation recursive generalized extended gradient identification method, a filtered auxiliary model recursive generalized extended least squares identification method, and a filtered auxiliary model multi-innovation recursive generalized extended least squares identification method by using the filtering identification idea and the auxiliary model identification idea. The proposed filtered auxiliary model recursive generalized extended identification methods can be generalized to other linear and nonlinear multivariable stochastic systems with colored noises.

Section: Filtered Auxiliary Model Recursive Generalized Extended Grad...mentioning

confidence: 99%

Filtered auxiliary model recursive generalized extended parameter estimation methods for Box–Jenkins systems by means of the filtering identification idea

Intl J Robust & Nonlinear

Xiao

et al. 2023

114

“…The methods proposed in this paper can combine some statistical tools and optimal strategies [76][77][78][79][80][81][82][83] to study the parameter estimation algorithms of linear and nonlinear systems [84][85][86][87] and can be applied to other fields [88][89][90][91][92] such as paper-making systems, information processing, engineering systems and so on. The steps of computing the parameter estimation vector θl (t) by the AM-HMIGI algorithm in ( 77)-( 95) are listed as follows.…”

Section: The Am-hmigi Algorithmmentioning

confidence: 99%

Hierarchical iterative identification algorithms for a nonlinear system with dead‐zone and saturation nonlinearity based on the auxiliary model

Sun

Wang

Adaptive Control & Signal

2023

Summary This paper investigates the identification problem of an output‐error nonlinear system with saturation and dead‐zone nonlinearity. Introducing a switching function and by means of the auxiliary model identification idea, an auxiliary model hierarchical least squares‐based iterative algorithm is proposed for estimating the parameters of the nonlinear system. Based on the hierarchical identification model, an auxiliary model hierarchical gradient‐based iterative algorithm is presented for the nonlinear system by utilizing the gradient search. In order to take full advantage of the system data, an auxiliary model hierarchical multi‐innovation gradient‐based iterative algorithm is derived for the nonlinear system according to the multi‐innovation identification theory. Finally, the numerical simulation results illustrate the effectiveness of the proposed algorithms.

“…The proposed algorithm in this paper can combine some statistical tools and optimal strategies [77][78][79][80][81][82][83][84][85][86] to study the parameter estimation algorithms of various stochastic systems with disturbances [87][88][89][90] and can be applied to literatures [91][92][93][94][95] such as paper-making systems and information processing systems and so on. The computing procedures of the MIGI algorithm in ( 10)-( 26) are shown in Figure 1.…”

Section: The Multi-innovation Gradient-based Iterative Algorithmmentioning

confidence: 99%

Multi‐innovation gradient‐based iterative identification methods for feedback nonlinear systems by using the decomposition technique

Yang

Intl J Robust & Nonlinear

2023

This paper studies the parameter estimation problems of feedback nonlinear systems. Combining the multi-innovation identification theory with the negative gradient search, we derive a multi-innovation gradient-based iterative algorithm. In order to reduce the computational burden and further improve the parameter estimation accuracy, a decomposition multi-innovation gradient-based iterative algorithm is proposed by using the decomposition technique. The key is to transform an original system into two subsystems and to estimate the parameters of each subsystem, respectively. A simulation example is provided to demonstrate the effectiveness of the proposed algorithms.