Neural networks provide a range of powerful new techniques for solving problems in pattern recognition, data analysis, and control. They have several notable features including high processing speeds and the ability to learn the solution to a problem from a set of examples. The majority of practical applications of neural networks currently make use of two basic network models. We describe these models in detail and explain the various techniques used to train them. Next we discuss a number of key issues which must be addressed when applying neural networks to practical problems, and highlight several potential pitfalls. Finally, we survey the various classes of problem which may be addressed using neural networks, and we illustrate them with a variety of successful applications drawn from a range of fields. It is intended that this review should be accessible to readers with no previous knowledge of neural networks, and yet also provide new insights for those already making practical use of these techniques.
Skip Nav Destination
Article navigation
June 1994
Brief Report|
June 01 1994
Neural networks and their applications
Chris M. Bishop
Chris M. Bishop
Neural Computing Research Group, Department of Computer Science and Applied Mathematics, Aston University, Birmingham, B4 7ET, United Kingdom
Search for other works by this author on:
Rev. Sci. Instrum. 65, 1803–1832 (1994)
Article history
Received:
August 16 1993
Accepted:
March 01 1994
Citation
Chris M. Bishop; Neural networks and their applications. Rev. Sci. Instrum. 1 June 1994; 65 (6): 1803–1832. https://doi.org/10.1063/1.1144830
Download citation file:
Pay-Per-View Access
$40.00
Sign In
You could not be signed in. Please check your credentials and make sure you have an active account and try again.
Citing articles via
Overview of the early campaign diagnostics for the SPARC tokamak (invited)
M. L. Reinke, I. Abramovic, et al.
An instrumentation guide to measuring thermal conductivity using frequency domain thermoreflectance (FDTR)
Dylan J. Kirsch, Joshua Martin, et al.
A glovebox-integrated confocal microscope for quantum sensing in inert atmosphere
Kseniia Volkova, Abhijeet M. Kumar, et al.
Related Content
Study of a thermoacoustic prime mover below onset of self‐oscillation
J Acoust Soc Am (February 1992)
The Development of Laser Method of Rare Isotope Al‐26 Detection as Applied for Environmental Problems
AIP Conference Proceedings (January 1997)
Structural study of a Si(001) grating surface by white beam x‐ray Laue photography
Appl. Phys. Lett. (June 1994)
1D GAS‐DYNAMIC SIMULATION OF SHOCK‐WAVE PROCESSES VIA INTERNET
AIP Conference Proceedings (December 2009)
Responses of green treefrogs (Hyla cinerea) to a graded series of synthetic calls
J Acoust Soc Am (August 2005)