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Axioms
Special Issues
Advanced Computing Methods for Fuzzy Systems and Neural Networks
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Advanced Computing Methods for Fuzzy Systems and Neural Networks

A special issue of Axioms (ISSN 2075-1680). This special issue belongs to the section "Mathematical Analysis".

Deadline for manuscript submissions: 30 November 2024 | Viewed by 2944

Special Issue Editors

Dr. Ebrahim Navid Sadjadi

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Guest Editor
Department of Informatics, Universidad Carlos III de Madrid, Madrid, Spain
Interests: fuzzy systems; neural networks; control theory; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Ahmad Fakharian

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Guest Editor
Department of Electrical Engineering, Islamic Azad University, Qazvin Branch, Qazvin 34185-1416, Iran
Interests: control and optimization microgrids; modelling and control; robotic systems; biologic systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Raúl Suárez

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Guest Editor
Instituto de Organización y Control de Sistemas Industriales (IOC), Universidad Politécnica de Cataluña (UPC), Barcelona, Spain
Interests: robotics
Dr. Farhad Shahnia

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Guest Editor
Discipline of Engineering and Energy, Murdoch University, Murdoch 6150, Australia
Interests: electric distribution systems power; microgrids; smart-grid-distributed energy resources
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Fuzzy Systems and Artificial Intelligence (AI) has wide applications in different control systems and optimization algorithms. AI is widely used in microgrids, robots, biologic systems, etc. Hence, the current Special Issue on Advanced Computing Methods for Fuzzy systems and Neural Networks comprises topics related to recent developments in Artificial Intelligence including：

  • Machine learning (neural networks, deep nets, reinforcement learning, federated learning);
  • Knowledge representation in machines;
  • Reasoning (rule-based systems, logical systems, fuzzy systems, formal specification and verification);
  • Mathematical analysis, mathematical programming, functional analysis, constraint satisfaction;
  • Heuristic methods (Bio- and nature-inspired computing, theorem proving);
  • Imprecise and uncertain reasoning (Bayesian, fuzzy approaches, etc.);
  • High-performance computing methods, edge, fog, cloud computing, IoT;
  • Distributed ledgers and blockchains.

The engineering applications include:

  • Natural language and speech processing;
  • Computer vision;
  • Distributed multi-agent systems, unmanned vehicles;
  • Agent-based modelling and simulation;
  • Autonomous driving;
  • Renewable energy, smart grids, E-vehicles;
  • Smart homes, smart cities, and smart future;
  • Hazard modelling and mitigation;
  • Manufacturing and Industry 4.0;
  • Remote sensing and operations.

We look forward to receiving your contributions. 

Dr. Ebrahim Navid Sadjadi
Dr. Ahmad Fakharian
Prof. Dr. Raúl Suárez
Dr. Farhad Shahnia
Guest Editors

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. Axioms is an international peer-reviewed open access monthly 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 2400 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

  • computing methods
  • optimization
  • control systems
  • fuzzy systems and neural networks
  • deep nets
  • machine learning and AI
  • mechatronics and robotic
  • IoT and smart devices
  • biologic systems
  • renewable energy resources and microgrid

Published Papers (2 papers)

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Research

13 pages, 3237 KiB  
Article
A Novel Methodology for Forecasting Business Cycles Using ARIMA and Neural Network with Weighted Fuzzy Membership Functions
by Soo H. Chai, Joon S. Lim, Heejin Yoon and Bohyun Wang
Axioms 2024, 13(1), 56; https://0-doi-org.brum.beds.ac.uk/10.3390/axioms13010056 - 18 Jan 2024
Viewed by 954
Abstract
Economic forecasting is crucial since it benefits many different parties, such as governments, businesses, investors, and the general public. This paper presents a novel methodology for forecasting business cycles using an autoregressive integrated moving average (ARIMA), a popular linear model in time series [...] Read more.
Economic forecasting is crucial since it benefits many different parties, such as governments, businesses, investors, and the general public. This paper presents a novel methodology for forecasting business cycles using an autoregressive integrated moving average (ARIMA), a popular linear model in time series forecasting, and a neural network with weighted fuzzy membership functions (NEWFM) as a forecasting model generator. The study used a dataset that included seven components of the leading composite index, which is used to predict positive or negative trends in several economic sectors before the GDP is compiled. The preprocessed time series data comprising the leading composite index using ARIMA were used as input vectors for the NEWFM to predict comprehensive business fluctuations. The prediction capability significantly improved through the duplicated refining process of the dataset using ARIMA and NEWFM. The combined ARIMA and NEWFM techniques exceeded ARIMA in both classification and prediction, yielding an accuracy of 91.61%. Full article
(This article belongs to the Special Issue Advanced Computing Methods for Fuzzy Systems and Neural Networks)
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21 pages, 4864 KiB  
Article
Multi-Step Prediction of Typhoon Tracks Combining Reanalysis Image Fusion Using Laplacian Pyramid and Discrete Wavelet Transform with ConvLSTM
by Peng Lu, Mingyu Xu, Ming Chen, Zhenhua Wang, Zongsheng Zheng and Yixuan Yin
Axioms 2023, 12(9), 874; https://0-doi-org.brum.beds.ac.uk/10.3390/axioms12090874 - 12 Sep 2023
Viewed by 715
Abstract
Typhoons often cause huge losses, so it is significant to accurately predict typhoon tracks. Nowadays, researchers predict typhoon tracks with the single step, while the correlation of adjacent moments data is small in long-term prediction, due to the large step of time. Moreover, [...] Read more.
Typhoons often cause huge losses, so it is significant to accurately predict typhoon tracks. Nowadays, researchers predict typhoon tracks with the single step, while the correlation of adjacent moments data is small in long-term prediction, due to the large step of time. Moreover, recursive multi-step prediction results in the accumulated error. Therefore, this paper proposes to fuse reanalysis images at the similarly historical moment and predicted images through Laplacian Pyramid and Discrete Wavelet Transform to reduce the accumulated error. That moment is determined according to the difference in the moving angle at predicted and historical moments, the color histogram similarity between predicted images and reanalysis images at historical moments and so on. Moreover, reanalysis images are weighted cascaded and input to ConvLSTM on the basis of the correlation between reanalysis data and the moving angle and distance of the typhoon. And, the Spatial Attention and weighted calculation of memory cells are added to improve the performance of ConvLSTM. This paper predicted typhoon tracks in 12 h, 18 h, 24 h and 48 h with recursive multi-step prediction. Their MAEs were 102.14 km, 168.17 km, 243.73 km and 574.62 km, respectively, which were reduced by 1.65 km, 5.93 km, 4.6 km and 13.09 km, respectively, compared with the predicted results of the improved ConvLSTM in this paper, which proved the validity of the model. Full article
(This article belongs to the Special Issue Advanced Computing Methods for Fuzzy Systems and Neural Networks)
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