Scheduled System Maintenance:
Some services will be unavailable Sunday, March 29th through Monday, March 30th. We apologize for the inconvenience.
By Topic

Parallel Multi-Layer neural network architecture with improved efficiency

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)

Neural network research over the past 3 decades has resulted in improved designs and more efficient training methods. In today's high-tech world, many complex non-linear systems described by dozens of differential equations are being replaced with powerful neural networks, making neural networks increasingly more important. However, all of the current designs, including the Multi-Layer Perceptron, the Bridged Multi-Layer Perceptron, and the Fully-Connected Cascade networks have a very large number of weights and connections, making them difficult to implement in hardware. The Parallel Multi-Layer Perceptron architecture introduced in this article yields the first neural network architecture that is practical to implement in hardware. This new architecture significantly reduces the number of connections and weights and eliminates the need for cross-layer connections. Results for this new architecture were tested on parity-N problems for values of N up to 17. Theoretical results show that this architecture yields valid results for all positive integer values of N.

Published in:

Human System Interactions (HSI), 2011 4th International Conference on

Date of Conference:

19-21 May 2011