Journal Information
IEEE Transactions on Neural Networks and Learning Systems
http://cis.ieee.org/ieee-transactions-on-neural-networks-and-learning-systems.html
Impact Factor:
4.854
Publisher:
IEEE
ISSN:
1045-9227
Viewed:
12538
Tracked:
18

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Call For Papers
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.

TNNLS publishes three types of articles:

    Papers (Full Papers)
    Brief Papers
    Comments Papers and Communications

Full Papers are characterized by novel contributions of archival nature in developing theories and/or innovative applications of neural networks and learning systems. The contribution should not be of incremental nature, but must present a well-founded and conclusive treatment of a problem. Well organized survey of literature on topics of current interest may also be considered. Usually a full paper will not exceed 13 pages of formatted text in the IEEE two-column style. Survey papers are excluded from this constraint.

Brief Papers report sufficiently interesting new theories and/or developments on previously published work in neural networks and related areas. For example, brief papers may report an extension of previous results or algorithms, innovative applications of a known approach to interesting problems, brief theoretical results, etc. The contribution should be conclusive and useful. A brief paper will not exceed 6 pages of formatted text in the IEEE two-column style.

Comments Papers and Communications are short articles which may be commenting on an error one has found in, or a significant disagreement one has with, a previously published paper. Typically, a comments paper is assigned to the same Associate Editor who handled the published paper being commented on. If the Associate Editor who was handling the previously published paper is no longer available, the Editor-in-Chief will assign the comments paper to another Associate Editor whose expertise closely matches the paper's topic. Comments papers and communications should comprise a significant contribution of interest to the TNNLS readership. The authors of the original paper may be invited to submit a rebuttal. A comments paper should be as concise as possible and will not exceed 3 pages formatted in the IEEE two-column style.

During the review process, submitted manuscripts will NOT be transferred from the Full Paper category to the Brief Paper category after submission/review. It would be the responsibility of authors to decide the category of their manuscript at the time of submission. If a manuscript is reviewed as a Paper and at the end of the review process, Reviewers/Associate Editor/Editor-in-Chief find it not suitable as a Full Paper but is a potential candidate for a Brief Paper, then the manuscript has to be resubmitted as a Brief Paper after revision, if authors desire to do so. Review management for Papers and Brief Papers is under the direction of an Associate Editor, who will normally solicit four reviews and wait for at least three responses before a decision is reached. To avoid delay in processing your paper, please follow closely these guidelines.
Last updated by Dou Sun in 2017-05-16
Special Issues
Special Issue on Discriminative Learning for Model Optimization and Statistical Inference
Submission Date: 2017-07-15

Model optimization and statistical inference have played a central role in various applications of computational intelligence, data analytics, and computer vision. Traditional model-centric learning approaches require properly crafted optimization and inference algorithms, as well as carefully tuned parameters. Recently, the discriminative learning technique has demonstrated its power for process-centric learning. The resulting solutions are closely related to a variety of statistical and optimization models such as sparse representation, structured regression, and conditional random fields, and are empowered by effective computational techniques such as bi-level optimization and partial differential equations (PDEs). Moreover, many deep learning models has been shown to be closely tied with discriminative learning models. For example, a problem-specific deep architecture can be formed by unfolding the model inference as an iterative process, whose parameters can be jointly learned from training data with a discriminative loss. Such a viewpoint motivates the incorporation of domain expertise and problem structures into designing deep architectures, and helps the interpretation and performance improvement of deep models. This special issue aims at promoting first-class research along this direction, and offers a timely collection of information to benefit the researchers and practitioners. We welcome high-quality original submissions addressing both novel theoretical and modeling progress, and real-world applications that benefit discriminative learning for model optimization and statistical inference. Topics of interests include, but are not limited to: - Task-driven learning for model optimization and/or statistical inference. - Novel architectures and algorithms for bi-level optimization and/or PDEs . - Problem-specific deep architectures for solving model optimization and statistical inference. - Integration of optimization-based, statistical learning, and inference models with deep learning models. - Sparse representation motivated deep architectures. - Structured regression motivated deep architectures. - Conditional random forest motivated recurrent neural networks. - Novel interpretative frameworks on the working mechanism of representative deep learning models. - Theoretical analysis of deep learning models and algorithms: convergence, optimality, generalization, stability, and sensitivity analysis. - Applications based on the above described models and algorithms: (1) image enhancement, restoration and synthesis; (2) optical flow, stereo matching, camera localization, and normal estimation; (3) visual recognition, detection, and segmentation, and scene understanding; (4) pattern classification, clustering and dimensionality reduction; (5) medical image analysis and other novel application domains
Last updated by Dou Sun in 2017-05-16
Special Issue on Intelligent Control through Neural Learning and Optimization for Human Machine Hybrid Systems
Submission Date: 2017-11-15

During the recent decades, there are a vast number of learning methods for designing intelligent controllers for human machine hybrid systems. However, a further consideration not only is guaranteeing the control stability, but is optimality based on a predefined cost function to determine the performance of the human machine hybrid systems. Therefore, we are faced with a need for improved control schemes, which not only achieve the stability of the human machine hybrid systems, but also keep the cost of the systems as small as possible. The special issue addresses a broad spectrum of topics ranging from deterministic and stochastic intelligent control design for various human machine hybrid systems such as unmanned aerial vehicles, intelligent quadruped robots, industrial robots, robotic exoskeletons, biped robots, wheeled balance transporters, to the optimization of the learning algorithm. Special attention should be given to how to optimize controller design, achieve the high accurate performance for the human machine interactions, and handle nonlinearities and the unknown system dynamics. This includes modeling, learning control, neural network adaptations, iterative learning, deep learning, reinforcement learning, dynamic programming and testing the effectiveness of the controllers. The special issue publishes original papers of innovative ideas and concepts, new discoveries and improvements, and novel applications to the field of intelligent control for human machine hybrid systems. Topics explored in this special issue include, but are not limited to: - Learning and Optimizations for the intelligent control; - Adaptive dynamic programming for human machine hybrid systems and their applications; - Iterative learning control for human machine hybrid systems and their applications; - Deep Learning for human machine interactions; - Reinforcement learning to handle nonlinearities for human machine hybrid systems; - Learning control design for intelligent robots; - High accurate tracking control via learning for multi-robot systems and applications; - Modeling and learning control for humanoid robots and applications; - Identification and learning control design for quadruped robots; - System design and learning control for industrial robots; - Learning control and optimizations for exoskeletons; - Learning control and balance analysis for wheeled balance transporters; - Learning control and optimizations for aerial vehicles; - Learning control and stability analysis for humanoid robots or quadruped robots; - Modeling, identification and optimizations via learning; - Neural network control and practical applications in model-free environment; - New applications of learning control for human machine hybrid systems.
Last updated by Dou Sun in 2017-05-16
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