Accession Number:

AD0641178

Title:

AN ANALOG LINEAR CLASSIFICATION NETWORK.

Descriptive Note:

Final rept.,

Corporate Author:

NAVAL ORDNANCE LAB WHITE OAK MD

Personal Author(s):

• BIEGALSKI,Robert J.

Report Date:

1966-04-27

Pagination or Media Count:

41.0

Abstract:

A linear network was built to classify analog signals consisting of a large number of parallel inputs. These inputs were derived by a feature abstracting system using synthetic nerve networks. In this classification network the signals pass simultaneously through a maximum amplitude filter and then are classified by a resistive memory matrix. The maximum amplitude filter attenuates smaller inputs much more than larger ones and serves to pick out predominant features. This is a way of utilizing the pandemonium concept of Selfridge. Classification by linear hyperplanes is discussed briefly and then the operation and design of the maximum amplitude filter is covered. Author

Descriptors:

• *NETWORKS
• *PATTERN RECOGNITION
• ANALOG SYSTEMS
• LINEAR SYSTEMS
• NERVE CELLS
• MATHEMATICAL MODELS
• SIGNALS
• CLASSIFICATION
• LEARNING MACHINES

Subject Categories:

• Computer Systems
• Bionics

Distribution Statement:

APPROVED FOR PUBLIC RELEASE