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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 2021

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 for the IEEE Transactions on Neural Networks and Learning Systems

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 <ieeetnnls@cqu.edu.cn>) 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: 15 May 2022

Notifications: 15 June 2022

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 "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: October 30, 2022 [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]

IEEE TNNLS Special Issue on "Explainable and Generalizable Deep Learning for Medical Imaging," Guest Editors: Tianming Liu, University of Georgia, USA; Dajiang Zhu, University of Texas at Arlington, USA; Fei Wang, Cornell University, USA; Islem Rekik, Istanbul Technical University, Turkey; Xia Hu, Rice University, USA; Dinggang Shen, ShanghaiTech University, China. Submission Deadline: September 1, 2022. [Call for Papers]

IEEE TNNLS Special Issue on "Explainable Representation Learning-based Intelligent Inspection and Maintenance of Complex Systems," Guest Editors: Zhigang Liu, Tongji University, Southwest Jiaotong University, China; Cesare Alippi, Università della Svizzera italiana, Switzerland and Politecnico di Milano, Italy, Hongtian Chen University of Alberta, Canada, Derong Liu University of Illinois at Chicago, USA. Submission Deadline: September 1, 2022. [Call for Papers]

IEEE TNNLS Special Issue on "Reinforcement Learning Based Control: Data-Efficient and Resilient Methods," Guest Editors: Weinan Gao, Florida Institute of Technology, USA; Li Na, Harvard University, USA; Kyriakos Vamvoudakis, Georgia Institute of Technology, USA; F. Richard Yu, Carleton University, Canada; Zhong-Ping Jiang, New York University, USA. Submission Deadline: April 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)
IEEE Xplore Link: https://ieeexplore.ieee.org/document/8528554