scholarly journals Adjoint least mean square control for solar photovoltaic array grid‐connected energy generating system

Author(s):  
Sunaina Singh ◽  
Seema Kewat ◽  
Bhim Singh ◽  
Bijaya Ketan Panigrahi
2013 ◽  
Vol 32 (7) ◽  
pp. 2078-2081
Author(s):  
Cheng-xi WANG ◽  
Yi-an LIU ◽  
Qiang ZHANG

2021 ◽  
Vol 11 (12) ◽  
pp. 5723
Author(s):  
Chundong Xu ◽  
Qinglin Li ◽  
Dongwen Ying

In this paper, we develop a modified adaptive combination strategy for the distributed estimation problem over diffusion networks. We still consider the online adaptive combiners estimation problem from the perspective of minimum variance unbiased estimation. In contrast with the classic adaptive combination strategy which exploits orthogonal projection technology, we formulate a non-constrained mean-square deviation (MSD) cost function by introducing Lagrange multipliers. Based on the Karush–Kuhn–Tucker (KKT) conditions, we derive the fixed-point iteration scheme of adaptive combiners. Illustrative simulations validate the improved transient and steady-state performance of the diffusion least-mean-square LMS algorithm incorporated with the proposed adaptive combination strategy.


Pramana ◽  
2021 ◽  
Vol 95 (3) ◽  
Author(s):  
Anjana Kumari ◽  
Yash Keju Barapatre ◽  
Swetaleena Sahoo ◽  
Sarita Nanda

Author(s):  
Jawwad Ahmad ◽  
Muhammad Zubair ◽  
Syed Sajjad Hussain Rizvi ◽  
Muhammad Shafique Shaikh

2020 ◽  
Vol 67 (12) ◽  
pp. 3602-3606 ◽  
Author(s):  
Sankha Subhra Bhattacharjee ◽  
Dwaipayan Ray ◽  
Nithin V. George

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Zahra Khandan ◽  
Hadi Sadoghi Yazdi

Kernel-based neural network (KNN) is proposed as a neuron that is applicable in online learning with adaptive parameters. This neuron with adaptive kernel parameter can classify data accurately instead of using a multilayer error backpropagation neural network. The proposed method, whose heart is kernel least-mean-square, can reduce memory requirement with sparsification technique, and the kernel can adaptively spread. Our experiments will reveal that this method is much faster and more accurate than previous online learning algorithms.


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