An Approximate Distributed Gradient Estimation Method for Networked System Optimization Under Limited Communications

2020 ◽  
Vol 50 (12) ◽  
pp. 5142-5151
Author(s):  
Jing Wang ◽  
Khanh D. Pham
Keyword(s):  
Estimation Method ◽  
System Optimization ◽  
Gradient Estimation ◽  
Networked System

scholarly journals Blind Signal Processing Algorithmsbased based on Recursive Gradient Estimation

2015 ◽  
Vol 5 (3) ◽  
pp. 548
Author(s):  
Namyong Kim ◽  
Mingoo Kang
Keyword(s):  
Computational Complexity ◽  
Euclidean Distance ◽  
Estimation Method ◽  
Processing Method ◽  
Gradient Estimation ◽  
Blind Signal Processing ◽  
Dirac Delta ◽  
Blind Signal ◽  
Gradient Calculation ◽  
Delta Functions

Blind algorithms based on the Euclidean distance (ED) between the output distribution function and a set of Dirac delta functions have a heavy computational burden of  due to some double summation operations for the sample size and symbol points. In this paper, a recursive approach to the estimation of the ED and its gradient is proposed to reduce the computational complexity for efficient implementation of the algorithm. The ED of the algorithm is comprised of information potentials (IPs), and the IPs at the next iteration can be calculated recursively based on the currently obtained IPs. Utilizing the recursively estimated IPs, the next step gradient for the weight update of the algorithm can be estimated recursively with the present gradient. With this recursive approach, the computational complexity of gradient calculation has only . The simulation results show that the proposed gradient estimation method holds significantly reduced computational complexity keeping the same performance as the block processing method

Extension of the phase and amplitude gradient estimation method for acoustic intensity to narrowband sources

2017 ◽  
Vol 141 (5) ◽  
pp. 3796-3796
Author(s):  
Kelli Succo ◽  
Scott D. Sommerfeldt ◽  
Kent L. Gee ◽  
Tracianne B. Neilsen
Keyword(s):  
Estimation Method ◽  
Gradient Estimation ◽  
Acoustic Intensity

Coaches estimation method for the athletes (CEM)

1995 ◽  
Author(s):  
Nagykaldi Csaba ◽  
Manohar Singh Badhan
Keyword(s):  
Estimation Method

Theoretical, methodological and practical issues of choice models

2018 ◽  
Vol 1 (1) ◽  
pp. 21-37
Author(s):  
Bharat P. Bhatta
Keyword(s):  
Discrete Choice ◽  
Model Building ◽  
Estimation Method ◽  
Choice Models ◽  
Discrete Choice Models ◽  
Decision Makers ◽  
Model Formulation ◽  
Random Utility Maximization ◽  
The Relationship ◽  
Method Model

This paper analyzes and synthesizes the fundamentals of discrete choice models. This paper alsodiscusses the basic concept and theory underlying the econometrics of discrete choice, specific choicemodels, estimation method, model building and tests, and applications of discrete choice models. Thiswork highlights the relationship between economic theory and discrete choice models: how economictheory contributes to choice modeling and vice versa. Keywords: Discrete choice models; Random utility maximization; Decision makers; Utility function;Model formulation

An Absolute Phase Estimation Method for Interferometry Imagery

2019 ◽  
Vol 1 (2) ◽  
pp. 14-19
Author(s):  
Sui Ping Lee ◽  
Yee Kit Chan ◽  
Tien Sze Lim
Keyword(s):  
Noise Suppression ◽  
Estimation Method ◽  
Phase Image ◽  
Phase Estimation ◽  
Phase Reconstruction ◽  
Estimation Algorithms ◽  
Absolute Phase ◽  
Filtering Rate ◽  
Interferometric Phase ◽  
Image Performance

Accurate interpretation of interferometric image requires an extremely challenging task based on actual phase reconstruction for incomplete noise observation. In spite of the establishment of comprehensive solutions, until now, a guaranteed means of solution method is yet to exist. The initially observed interferometric image is formed by 2π-periodic phase image that wrapped within (-π, π]. Such inverse problem is further corrupted by noise distortion and leads to the degradation of interferometric image. In order to overcome this, an effective algorithm that enables noise suppression and absolute phase reconstruction of interferometric phase image is proposed. The proposed method incorporates an improved order statistical filter that is able to adjust or vary on its filtering rate by adapting to phase noise level of relevant interferometric image. Performance of proposed method is evaluated and compared with other existing phase estimation algorithms. The comparison is based on a series of computer simulated and real interferometric data images. The experiment results illustrate the effectiveness and competency of the proposed method.

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