Neural Network Application
Submission Deadline: Jan. 30, 2016
Lead Guest Editor
Fang Yi
College of Automotive Engineering, Shanghai University of Engineering and Science, Shanghai, China
Guest Editor
  • Hanfeng Chen
    College of Automotive Engineering, Shanghai University of Engineering and Science, Shanghai, China
Guidelines for Submission
Manuscripts can be submitted until the expiry of the deadline. Submissions must be previously unpublished and may not be under consideration elsewhere.
Papers should be formatted according to the guidelines for authors (see: By submitting your manuscripts to the special issue, you are acknowledging that you accept the rules established for publication of manuscripts, including agreement to pay the Article Processing Charges for the manuscripts. Manuscripts should be submitted electronically through the online manuscript submission system at All papers will be peer-reviewed. Accepted papers will be published continuously in the journal and will be listed together on the special issue website.
Published Papers
The special issue currently is open for paper submission. Potential authors are humbly requested to submit an electronic copy of their complete manuscript by clicking here.
With the continuous development of artificial neural network technology, the artificial neural network technology has also been used in many nonlinear system modeling and identification. Meanwhile, neural network with high accuracy, and have strong ability of distribution storage and learning, it also has strong ability of noise and fault tolerance, can fully close to the complex nonlinear relations, also have the function of associative memory. This special issue is to improve the algorithm of advanced research in the area of statistics that can fulfill big scale samples. It also can show high efficiency and high precision in machine vision category. Neural network also has more new application in some special area, so we can try to detect it.

Aims and Scope:

1. Using neural network to identification and recognition
2. The contrast between neural network and other methods
3. The improvement of neural network
4. The advantage and disadvantage on neural network
5. The application trend of neural network
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