Your Search Results

Use this resource - and many more! - in your textbook!

AcademicPub holds over eight million pieces of educational content for you to mix-and-match your way.

Experience the freedom of customizing your course pack with AcademicPub!
Not an educator but still interested in using this content? No problem! Visit our provider's page to contact the publisher and get permission directly.

A real-time implementable neural network

By: Ngolediage, J.E.; Dlay, S.S.; Naguib, R.N.G.;

1994 / IEEE / 0-7803-1901-X


This item was taken from the IEEE Periodical ' A real-time implementable neural network ' This paper describes a real-time implementable algorithm that takes advantage of the Lyapunov function, which guarantees an asymptotic behaviour of the solutions to differential equations. The algorithm is designed for feedforward neural networks. Unlike conventional backpropagation, it does not require the suite of derivatives to be propagated from the top layer to the bottom one. Consequently, the amount of circuitry required for an analogue CMOS implementation is minimal. In addition, each unit in the network has its output fed back to itself across a delay element. Results from an HSPICE simulation of the 2.4 micron CMOS architecture are presented.<>