Applied Fuzzy Logic and Soft Computing to Real World Problems

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

Deadline for manuscript submissions: 28 June 2024 | Viewed by 1204

Special Issue Editors


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Guest Editor
Software Engineering, Autonomous University of Baja California, Tijuana 21500, Mexico
Interests: bio-inspired computing; intelligent computing; intelligent embedded system; swarm intelligence

E-Mail Website
Guest Editor
Software Engineering, Autonomous University of Baja California, Tijuana 21500, Mexico
Interests: fuzzy logic; intelligent control; bio-inspired algorithms

Special Issue Information

Dear Colleagues,

Soft computing is a set of artificial intelligence techniques that focus on modeling and solving complex problems that are difficult to address with traditional mathematical methods based on rigid and precise rules. Soft computing is capable of handling imprecise, uncertain, or incomplete data and consists of three main approaches: fuzzy logic, neural networks, and evolutionary or bio-inspired computing.

Soft computing applications are numerous and are used in diverse fields such as medicine, engineering, economics, robotics, and data science. In medicine, for example, it is used to analyze medical images and to help doctors diagnose diseases such as cancer. In engineering, it is used to design automatic control systems that can adapt to variable conditions and can change their behavior according to the situation. In data mining, it is used to analyze large amounts of information and to extract patterns and trends that can be useful for making decisions.

In robotics, it is used to create robots that can interact with their environment in a more natural and adaptive way. Robots that use artificial intelligence techniques are able to learn and adapt to their environment, allowing them to perform complex tasks in dynamic and changing environments.

In conclusion, the use of intelligent computing techniques such as bio-inspired optimization algorithms, fuzzy logic control, and neural networks applied to problem-solving has achieved significant technological advancement. 

This Special Issue invites all researchers to report and share the results obtained from their research work. 

Potential themes include but are not limited to the following: 

  • Novel nature-inspired or application-inspired optimization algorithms;
  • Statistical approaches for understanding the behavior of nature-inspired methods;
  • Parameter adaptation using mathematical fuzzy models;
  • Intelligent agents;
  • Mathematical fuzzy logic and intelligent and automatic control;
  • Optimization of neurocomputing systems;
  • Methods based on collective intelligence;
  • Artificial intelligence-based models;
  • Artificial neural networks;
  • Federated learning models. 

I/We look forward to receiving your contributions.

Dr. Camilo Caraveo
Dr. Leticia Cervantes
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

  • intelligent computing
  • fuzzy logic control
  • intelligent control
  • optimization problems
  • intelligent agents
  • neural networks

Published Papers (1 paper)

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Research

26 pages, 2626 KiB  
Article
A Fuzzy-Random Extension of Jamshidian’s Bond Option Pricing Model and Compatible One-Factor Term Structure Models
by Jorge de Andrés-Sánchez
Axioms 2023, 12(7), 668; https://doi.org/10.3390/axioms12070668 - 6 Jul 2023
Viewed by 868
Abstract
The primary objective of this paper is to expand Jamshidian’s bond option formula and compatible one-factor term structure models by incorporating the existence of uncertainty in the parameters governing interest-rate fluctuations. Specifically, we consider imprecision in the parameters related to the speed of [...] Read more.
The primary objective of this paper is to expand Jamshidian’s bond option formula and compatible one-factor term structure models by incorporating the existence of uncertainty in the parameters governing interest-rate fluctuations. Specifically, we consider imprecision in the parameters related to the speed of reversion, equilibrium short-term interest rate, and volatility. To model this uncertainty, we utilize fuzzy numbers, which, in this context, are interpreted as epistemic fuzzy sets. The second objective of this study is to propose a methodology for estimating these parameters based on historical data. To do so, we use the possibility distribution functions capability to quantify imprecise probability distributions. Furthermore, this paper presents an application to the term structure of fixed-income bonds with the highest credit rating in the Euro area. This empirical application allows for evaluating the effectiveness of the fuzzy extension in fitting the dynamics of interest rates and assessing the suitability of the proposed extension. Full article
(This article belongs to the Special Issue Applied Fuzzy Logic and Soft Computing to Real World Problems)
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