New Research in Computational Intelligence

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 15 June 2026 | Viewed by 807

Special Issue Editors


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Guest Editor
School of AI and Advanced Computing, Xi’an Jiaotong-Liverpool University, Suzhou 215028, China
Interests: computational intelligence; decision-making methods; information fusion

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Guest Editor
School of AI and Advanced Computing, Xi’an Jiaotong-Liverpool University, Suzhou 215028, China
Interests: artificial intelligence; deep learning; explainable model; time series data

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Guest Editor
School of Artificial Intelligence and Advanced Computing (AIAC), Xi'an Jiaotong-Liverpool University, Suzhou 215412, China
Interests: statistical and structural pattern recognition; complex networks; machine learning for graphs and networks; thermodynamic and quantum statistics; information theory
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Special Issue Information

Dear Colleagues,

The Special Issue "New Research in Computational Intelligence" aims to explore cutting-edge advancements in computational intelligence methodologies and their applications in decision-making processes. In an increasingly complex and data-driven world, robust decision-making tools are paramount. This issue will highlight innovative computational techniques, including but not limited to machine learning, fuzzy logic, and neural networks, that enhance decision-making capabilities across various domains, such as finance, healthcare, and smart systems. Contributions will focus on theoretical frameworks, empirical studies, and case applications that demonstrate how computational intelligence can improve the quality and efficiency of decisions. By fostering interdisciplinary research, this Special Issue seeks to inspire novel approaches to tackle real-world challenges and promote the development of intelligent systems that support human decision-making.

Dr. Zhen Hua
Dr. Chaoqun Wang
Dr. Jianjia Wang
Guest Editors

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Keywords

  • computational intelligence
  • decision making
  • machine learning
  • neural networks
  • fuzzy logic
  • data-driven decision support
  • intelligent systems
  • optimization
  • empirical studies
  • interdisciplinary research

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Published Papers (1 paper)

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Research

26 pages, 1328 KB  
Article
Adaptive Electromagnetic Working Mode Decision-Making Algorithm for Miniaturized Radar Systems in Complex Electromagnetic Environments: An Improved Soft Actor–Critic Algorithm
by Houwei Liu, Chudi Zhang, Lulu Wang, Jun Hu and Shiyou Xu
Electronics 2025, 14(23), 4633; https://doi.org/10.3390/electronics14234633 - 25 Nov 2025
Viewed by 457
Abstract
With the advancement of multi-function radar (MFR) technology, miniaturized radar systems (MRSs) inevitably operate in complex electromagnetic environments (CEEs) dominated by MFRs as single-function radars are gradually being replaced by MFRs. MFRs can not only flexibly switch working states and generate diverse radar [...] Read more.
With the advancement of multi-function radar (MFR) technology, miniaturized radar systems (MRSs) inevitably operate in complex electromagnetic environments (CEEs) dominated by MFRs as single-function radars are gradually being replaced by MFRs. MFRs can not only flexibly switch working states and generate diverse radar signal characteristics, but they can also acquire the MRSs’ position information, which has a significant impact on the execution of the MRSs’ close-range remote sensing missions. For resource-constrained MRS, selecting the optimal electromagnetic working mode in such environments becomes a critical challenge. This paper addresses the adaptive electromagnetic working mode decision-making (EWMDM) problem for MRS in CEE by establishing an EWMDM model and proposing a reinforcement learning (RL) method based on an improved soft actor–critic algorithm with prioritized experience replay (SAC-PER). First, we simulate the process of MRS receiving pulse description words (PDWs) from MFR waveforms and introduce noise into the PDWs to emulate real electromagnetic environments. Then we use a threshold to filter out uncertain recognition results to reduce the impact of noise on the MFR’s working state recognition. Subsequently, we analyze the limitations of the SAC-PER algorithm in noisy environments and propose an improved algorithm—SAC with alpha decay prioritized experience replay (SAC-ADPER)—to address the influence of environmental noise and stochasticity. Experimental results show that SAC-ADPER significantly accelerates the convergence speed of EWMDM in noisy environments and validate the effectiveness of the proposed method. Full article
(This article belongs to the Special Issue New Research in Computational Intelligence)
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