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Article
Peer-Review Record

Study on Resistant Hierarchical Fuzzy Neural Networks

Electronics 2022, 11(4), 598; https://doi.org/10.3390/electronics11040598
by Fengyu Gao 1,2, Jer-Guang Hsieh 1, Ying-Sheng Kuo 3 and Jyh-Horng Jeng 4,*
Reviewer 1: Anonymous
Reviewer 2: Anonymous
Electronics 2022, 11(4), 598; https://doi.org/10.3390/electronics11040598
Submission received: 30 January 2022 / Revised: 8 February 2022 / Accepted: 13 February 2022 / Published: 15 February 2022
(This article belongs to the Topic Data Science and Knowledge Discovery)

Round 1

Reviewer 1 Report

In their work, the authors presented resistant hierarchical fuzzy neural networks and investigated the associated deep learning problems. In doing so, three different noise types were added to the output node of the neural network to test the resistance of the neural networks. However, from the simulation results it seems that the performance has very little connection with the types of the noise. Maybe I missed it, but why did you exactly chose these 10 datasets? How was the selection process? Some figures regarding the datasets would be nice, I don’t know all these datasets and what they “show”, e.g. are they all 2D or some 3D? At the end of the conclusion should be more statements about future work.

 

Author Response

Please see attachment.

Author Response File: Author Response.pdf

Reviewer 2 Report

  1. Figure 2. Fuzzy neural network. edit the caption
    2. Cite the relevant references in all the section
  2. 3. After 3 examples please mention a paragraph of comparison or conclude the results.
  3. There must be more references related to the literature review. 
  4. Improve the introduction and literature review part.

Author Response

Please see attachment.

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Thanks for addressing my comments, I endorse the paper for publication.

 

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