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Mathematics
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Theory and Applications of Neural Networks
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Theory and Applications of Neural Networks

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Engineering Mathematics".

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 5727

Special Issue Editor

Prof. Dr. Zhengqiu Zhang

E-Mail Website
Guest Editor
School of Mathematics, Hunan University, Changsha 410082, China
Interests: neural networks theory and applications (stability, synchronization, economic forecasting); difference equations; differential equations; mathematical modelling

Special Issue Information

Dear Colleagues,

After decades of development, neural network theory has achieved widespread success in many research fields such as pattern recognition, automatic control, signal processing, decision-making assistance, and artificial intelligence. With the continuous development of neural network theory itself and related theories and technologies, the application of neural networks will surely grow more in-depth. The aim of this Special Issue is to publish high-quality papers in the theory and applications of neural networks and to promote academic exchange between a wide array of scholars. As such, I am inviting you to submit an article to a Special Issue on the theory and applications of neural network in Mathematics, a peer-reviewed journal. Such articles can include theoretical research on neural networks, the optimization of neural network models, the research of neural network software simulation and hardware realization, or the application research of neural network in various fields.

Prof. Dr. Zhengqiu Zhang
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Neural networks
  • Deep learning
  • Artificial intelligence
  • Artificial neural networks
  • Connection model
  • Pattern recognition
  • Signal processing
  • Automatic control
  • Robot control
  • Optimization
  • Economic forecasting
  • Expert system
  • Stability, synchronization, dissipation of neural networks

Published Papers (3 papers)

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Research

35 pages, 1030 KiB  
Article
New Model of Heteroasociative Min Memory Robust to Acquisition Noise
by Julio César Salgado-Ramírez, Jean Marie Vianney Kinani, Eduardo Antonio Cendejas-Castro, Alberto Jorge Rosales-Silva, Eduardo Ramos-Díaz and Juan Luis Díaz-de-Léon-Santiago
Mathematics 2022, 10(1), 148; https://0-doi-org.brum.beds.ac.uk/10.3390/math10010148 - 04 Jan 2022
Cited by 3 | Viewed by 1346
Abstract
Associative memories in min and max algebra are of great interest for pattern recognition. One property of these is that they are one-shot, that is, in an attempt they converge to the solution without having to iterate. These memories have proven to be [...] Read more.
Associative memories in min and max algebra are of great interest for pattern recognition. One property of these is that they are one-shot, that is, in an attempt they converge to the solution without having to iterate. These memories have proven to be very efficient, but they manifest some weakness with mixed noise. If an appropriate kernel is not used, that is, a subset of the pattern to be recalled that is not affected by noise, memories fail noticeably. A possible problem for building kernels with sufficient conditions, using binary and gray-scale images, is not knowing how the noise is registered in these images. A solution to this problem is presented by analyzing the behavior of the acquisition noise. What is new about this analysis is that, noise can be mapped to a distance obtained by a distance transform. Furthermore, this analysis provides the basis for a new model of min heteroassociative memory that is robust to the acquisition/mixed noise. The proposed model is novel because min associative memories are typically inoperative to mixed noise. The new model of heteroassocitative memory obtains very interesting results with this type of noise. Full article
(This article belongs to the Special Issue Theory and Applications of Neural Networks)
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13 pages, 2676 KiB  
Article
Prediction of Kerf Width in Laser Cutting of Thin Non-Oriented Electrical Steel Sheets Using Convolutional Neural Network
by Dinh-Tu Nguyen, Jeng-Rong Ho, Pi-Cheng Tung and Chih-Kuang Lin
Mathematics 2021, 9(18), 2261; https://0-doi-org.brum.beds.ac.uk/10.3390/math9182261 - 15 Sep 2021
Cited by 9 | Viewed by 2008
Abstract
Kerf width is one of the most important quality items in cutting of thin metallic sheets. The aim of this study was to develop a convolutional neural network (CNN) model for analysis and prediction of kerf width in laser cutting of thin non-oriented [...] Read more.
Kerf width is one of the most important quality items in cutting of thin metallic sheets. The aim of this study was to develop a convolutional neural network (CNN) model for analysis and prediction of kerf width in laser cutting of thin non-oriented electrical steel sheets. Three input process parameters were considered, namely, laser power, cutting speed, and pulse frequency, while one output parameter, kerf width, was evaluated. In total, 40 sets of experimental data were obtained for development of the CNN model, including 36 sets for training with k-fold cross-validation and four sets for testing. Compared with a deep neural network (DNN) model and an extreme learning machine (ELM) model, the developed CNN model had the lowest mean absolute percentage error (MAPE) of 4.76% for the final test dataset in predicting kerf width. This indicates that the proposed CNN model is an appropriate model for kerf width prediction in laser cutting of thin non-oriented electrical steel sheets. Full article
(This article belongs to the Special Issue Theory and Applications of Neural Networks)
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20 pages, 4202 KiB  
Article
Multiple Loop Fuzzy Neural Network Fractional Order Sliding Mode Control of Micro Gyroscope
by Yunmei Fang, Fang Chen and Juntao Fei
Mathematics 2021, 9(17), 2124; https://0-doi-org.brum.beds.ac.uk/10.3390/math9172124 - 01 Sep 2021
Cited by 3 | Viewed by 1559
Abstract
In this paper, an adaptive double feedback fuzzy neural fractional order sliding control approach is presented to solve the problem that lumped parameter uncertainties cannot be measured and the parameters are unknown in a micro gyroscope system. Firstly, a fractional order sliding surface [...] Read more.
In this paper, an adaptive double feedback fuzzy neural fractional order sliding control approach is presented to solve the problem that lumped parameter uncertainties cannot be measured and the parameters are unknown in a micro gyroscope system. Firstly, a fractional order sliding surface is designed, and the fractional order terms can provide additional freedom and improve the control accuracy. Then, the upper bound of lumped nonlinearities is estimated online using a double feedback fuzzy neural network. Accordingly, the gain of switching law is replaced by the estimated value. Meanwhile, the parameters of the double feedback fuzzy network, including base widths, centers, output layer weights, inner gains, and outer gains, can be adjusted in real time in order to improve the stability and identification efficiency. Finally, the simulation results display the performance of the proposed approach in terms of convergence speed and track speed. Full article
(This article belongs to the Special Issue Theory and Applications of Neural Networks)
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