Robust channel estimation based on the maximum entropy principle

Hu, Zhengyang; Xue, Jiang; Li, Feng; Zhao, Qian; Meng, Deyu; Xu, Zongben

doi:10.1007/s11432-023-3822-9

Cited by 2 publications

(1 citation statement)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…are used to obtain the fourthorder central moment of the uncertain motor parameters to be identified based on the maximum entropy principle [24]. The relationship between the probability distribution and the fourth-order moment is shown in Eq.…”

Section: Uncertain Motor Parameter Described By the Fourth-order Momentmentioning

confidence: 99%

Random uncertain motor parameters identification based on fourth-order moment method and trust region management technology

Mao,

Liao,

Guo

et al. 2024

Preprint

View full text Add to dashboard Cite

In many engineering practices, motor parameters are usually uncertain due to complex objective environments or various subjective perceptions. Using the fourth-order moment method and trust region model management technology, the uncertain motor parameter identification problem is transformed into a deterministic parameter identification problem for motor statistical characteristic parameters. Firstly, the probability distribution function is used to measure the uncertainty. The cumulative probability under different motor performance response thresholds is calculated by the fourth-order moment method to obtain the corresponding probability distribution function. In the subsequent framework, the micro multi-objective genetic algorithm is used to minimize the calculated and measured cumulative probability of motor performance response. To improve the optimization efficiency, the trust region management technology, is guided to gradually approach the region where the real fourth-order motor statistical characteristic parameters are located. At the same time, the genetic intelligent technology is introduced to reduce the calculation cost. Finally, the numerical results in the example show that the proposed method can effectively identify the uncertain motor parameters.

show abstract

Section: Uncertain Motor Parameter Described By the Fourth-order Momentmentioning

confidence: 99%

Random uncertain motor parameters identification based on fourth-order moment method and trust region management technology

Mao,

Liao,

Guo

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

Random uncertain motor parameters identification combining fourth-order moment and trust region

Mao,

Liao,

Guo

et al. 2024

Sci Rep

View full text Add to dashboard Cite

In random uncertain motor parameter identification field, there is low identification efficiency and ill-conditioned data coming from the second iteration involved in the uncertainty propagation and surrogate model between the motor parameters and the performance response. In this paper, the fourth-order moment method and trust region model management technology are combined to reduce the dependence of the surrogate model accuracy and improve the computational efficiency. In the framework, the inner layer calculates the cumulative probability under different motor performance response thresholds based on the fourth-order moment method, and obtain the probability density function of the calculated motor performance response. The outer layer transforms the random uncertain motor parameters identification into a deterministic optimization problem by minimizing the probability distribution between the calculated and the measured motor performance response. In the outer layer optimization, the trust region model management technology is used to divide the search interval of the entire parameter to be identified into a series of trust regions, construct a surrogate model on the trust region to calculate the motor performance response, and continuously update the trust region by comparing the response residuals between the calculated and measured motor performance response, so that the motor parameters to be identified continue to approach the interval where true values are located. At the same time, the genetic intelligent technology is introduced to further reduce the calculation cost. Finally, the probability distribution of random uncertain motor parameters is obtained according to the identified mean, standard deviation, skewness and kurtosis coefficients. The numerical results in the example verify that the random uncertain motor parameter identification can be effectively achieved by the proposed method.

show abstract

Robust channel estimation based on the maximum entropy principle

Cited by 2 publications

References 32 publications

Random uncertain motor parameters identification based on fourth-order moment method and trust region management technology

Random uncertain motor parameters identification based on fourth-order moment method and trust region management technology

Random uncertain motor parameters identification combining fourth-order moment and trust region

Contact Info

Product

Resources

About