Synaptic Strengths For Neural Simulation Of The Traveling Salesman Problem

W. I. Clement; R. M. Inigo; E. S. McVey

doi:10.1117/12.946996

29 March 1988 Synaptic Strengths For Neural Simulation Of The Traveling Salesman Problem

W. I. Clement, R. M. Inigo, E. S. McVey

Proceedings Volume 0937, Applications of Artificial Intelligence VI; (1988) https://doi.org/10.1117/12.946996
Event: 1988 Technical Symposium on Optics, Electro-Optics, and Sensors, 1988, Orlando, FL, United States

Abstract

The use of neural-like networks to solve optimization problems such as the Traveling Salesman Problem has been proposed by Hopfield and Tank. The networks are based on a standard "neuron" which can be implemented by means of voltage amplifiers. The gain of network conductances and time constants as well as the value of constants in the problem's energy function have a decisive influence on the solution provided by the network, yet Hopfield and Tank do not make clear how to determine the value of these constants for a particular problem. In this paper a method for selection of constants is proposed which gives good results for the TSP. Instead of readjusting the gains and adding terms to the energy function until good results are obtained, the gains are chosen a priori and the energy function's original form is not altered. Simulation results are presented and discussed.

Citation Download Citation

W. I. Clement, R. M. Inigo, and E. S. McVey "Synaptic Strengths For Neural Simulation Of The Traveling Salesman Problem", Proc. SPIE 0937, Applications of Artificial Intelligence VI, (29 March 1988); https://doi.org/10.1117/12.946996

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available