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The IEEE Transactions on Neural Networks and Learning Systems publishes technical articles that deal with the theory, design, and applications of neural networks and related learning systems.

Impact Score

TNNLS Impact Score 2023





The values displayed for the journal bibliometrics fields in IEEE Xplore are based on the Journal Citation Report from Clarivate from the 2022 report released in June 2023. The values displayed for CiteScore metrics are from Scopus 2022 report released in June 2023. Journal Citation Metrics Journal Citation Metrics such as Impact Factor, Eigenfactor Score™ and Article Influence Score™ are available where applicable. Each year, Journal Citation Reports© (JCR) from Thomson Reuters examines the influence and impact of scholarly research journals. JCR reveals the relationship between citing and cited journals, offering a systematic, objective means to evaluate the world's leading journals.  Find out more about IEEE Journal Bibliometrics

Call for Associate Editors

Please note that new positions will become available for joining the Editorial Board of the IEEE Transactions on Neural Networks and Learning Systems (TNNLS). Candidates interested in applying must submit via email, in a single PDF file, to the Editor-in-Chief of TNNLS (Prof. Yongduan Song <>) the following information:

  • A detailed CV in English. It's particularly important that you clearly mark your journal publications related to neural networks and learning systems. Also, please provide details of your academic/industrial credentials as well as your affiliation, including your current position. It is desirable that you also provide an institutional webpage whenever possible.
  • Publications in TNNLS (particularly in the last 5 years). Please provide as much detail as possible.
  • Special issues at TNNLS and/or Special Sessions that you had organized at the IEEE congress on NN and the related learning systems. Provide as much detail as possible including the names of your co-organizers/guest co-editors as well as the number of papers submitted and accepted.
  • IEEE CIS Membership (are you currently an IEEE and/or CIS member)? If so, what is your status? Member, Senior Member, Fellow).
  • From 4 to 8 areas of expertise within neural networks and learning systems in which you could handle submissions (please indicate relevant publications from your CV in each of the areas selected).
  • Prior experience as a reviewer for TNNLS. Have you reviewed papers for TNNLS? If so, since what year or within what time period? Please provide as much detail as possible.

Deadline for applying

12 December, 2023


12 January, 2024

Please note that all applications will be carefully reviewed considering several elements, including: prior editorial experience, topics of expertise, and publications record. Note however that all candidates who get pre-selected are subject to the approval of the Vice-President for Publications and of the President of the IEEE Computational Intelligence Society. Female candidates and people with affiliations in industry and/or government are strongly encouraged to apply.

Call for Special Issues

IEEE TNNLS Special issue on "Trustworthy Federated Learning" Guest Editors:  Qiang Yang, Hong Kong University of Science and Technology, Han Yu, Nanyang Technological University, Singapore, Sin G. Teo, Agency for Science, Technology and Research, Singapore, Bo Li, University of Illinois Urbana-Champaign, USA, Guodong Long, University of Sydney, Australia, Chao Jin, Agency for Science, Technology and Research, Singapore, Lixin Fan, WeBank, China, Yang Liu, Tsinghua University, China, Le Zhang, University of Electronic Science and Technology of China. Submission Deadline: June 1, 2023 [Call for Papers]

IEEE TNNLS Special Issue on "Deep Learning: When and How?" Guest Editors:Bart Baesens, KU Leuven, Belgium, María Óskarsdóttir, Reykjavik University, Iceland, Davide Bacciu, Universita' di Pisa, Italy, Hugo Jair Escalante, INAOE, Mexico, Rohitash Chandra, UNSW, Australia. Submission Deadline: April 23, 2023 [Call for Papers]

IEEE TNNLS Special Issue on "Graph Learning" Guest Editors: Feng Xia, RMIT University, Australia, Renaud Lambiotte, University of Oxford, United Kingdom, Neil Shah, Snap Research, USA, Hanghang Tong, University of Illinois Urbana-Champaign, USA, Irwin King, The Chinese University of Hong Kong, Hong Kong. Submission Deadline: July 1, 2023 [Call for Papers

IEEE TNNLS Special Issue on "Learning Theories and Methods with Application to Digitized Process Manufacturing" Guest Editors: Feng Qian, East China University of Science and Technology, China, Yaochu Jin, Bielefeld University, Germany, Xinghuo Yu, Royal Melbourne Institute of Technology University, Australia, Yang Tang, East China University of Science and Technology, China, Guy B. Marin, Ghent University, Belgium. Submission Deadline: March 31, 2023 [Call for Papers

IEEE TNNLS Special Issue on "Deep Learning for Intelligent Media Computing and Applications" Guest Editors: Hamido Fujita, Iwate Prefectural University, Japan, Bo Li, Northwestern Polytechnical University, China, Yiyu Yao, University of Regina, Canada, Xinbo Gao, Chongqing University of Posts and Telecommunications, China, Maoguo Gong, Xidian University, China, Ivan Lee, University of South Australia, Australia, Martin Ester, Simon Fraser University, Canada, Xin Wang, Tsinghua University, China. Submission Deadline: January 31, 2023 [Call for Papers

IEEE TNNLS Special Issue on "Information Theoretic Methods for the Generalization, Robustness and Interpretability of Machine Learning" Guest Editors: Badong Chen, Xi’an Jiaotong University, China, Shujian Yu, UiT – The Arctic University of Norway, Norway, Robert Jenssen, UiT – The Arctic University of Norway, Norway, Jose C. Principe, University of Florida, USA, Klaus-Robert Müller, Technische Universität Berlin (TU Berlin), Germany. Submission Deadline: October 1, 2022 [Call for Papers]

Featured Paper

The Boundedness Conditions for Model-Free HDP( λ )
Authors: Seaar Al-Dabooni, Donald Wunsch
Publication: IEEE Transactions on Neural Networks and Learning Systems (TNNLS)
Issue: Volume 30, Issue 7 – July 2019
Pages: 1928-1942

Abstract: This paper provides the stability analysis for a model-free action-dependent heuristic dynamic programing (HDP) approach with an eligibility trace long-term prediction parameter (λ). HDP(λ) learns from more than one future reward. Eligibility traces have long been popular in Q-learning. This paper proves and demonstrates that they are worthwhile to use with HDP. In this paper, we prove its uniformly ultimately bounded (UUB) property under certain conditions. Previous works present a UUB proof for traditional HDP [HDP(λ = 0)], but we extend the proof with the λ parameter. By using Lyapunov stability, we demonstrate the boundedness of the estimated error for the critic and actor neural networks as well as learning rate parameters. Three case studies demonstrate the effectiveness of HDP(λ). The trajectories of the internal reinforcement signal nonlinear system are considered as the first case. We compare the results with the performance of HDP and traditional temporal difference [TD(λ)] with different λ values. The second case study is a single-link inverted pendulum. We investigate the performance of the inverted pendulum by comparing HDP(λ) with regular HDP, with different levels of noise. The third case study is a 3-D maze navigation benchmark, which is compared with state action reward state action, Q(λ), HDP, and HDP(λ). All these simulation results illustrate that HDP(λ) has a competitive performance; thus this contribution is not only UUB but also useful in comparison with traditional HDP.

Index Terms: λ-return, action dependent (AD), approximate dynamic programing (ADP), heuristic dynamic programing (HDP), Lyapunov stability, model free, uniformly ultimately bounded (UUB)
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