We are currently experiencing intermittent issues impacting performance. We apologize for the inconvenience.
By Topic

Signal Processing Magazine, IEEE

Issue 1 • Date Jan. 1993

Filter Results

  • Progress in supervised neural networks

    Publication Year: 1993 , Page(s): 8 - 39
    Cited by:  Papers (286)  |  Patents (16)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3141 KB)  

    Theoretical results concerning the capabilities and limitations of various neural network models are summarized, and some of their extensions are discussed. The network models considered are divided into two basic categories: static networks and dynamic networks. Unlike static networks, dynamic networks have memory. They fall into three groups: networks with feedforward dynamics, networks with output feedback, and networks with state feedback, which are emphasized in this work. Most of the networks discussed are trained using supervised learning.<> View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.

Aims & Scope

IEEE Signal Processing Magazine publishes tutorial-style articles on signal processing research and applications, as well as columns and forums on issues of interest.

Full Aims & Scope

Meet Our Editors

Min Wu
University of Maryland, College Park
United States