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Biology Inspired Approximate Data Representation for Signal Processing, Soft Computing and Control Applications

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Emil M. Petriu
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Abstract:

This paper reviews basics, similarities, and applications of two well-known biology inspired approximate data representation modalities: stochastic data representation an...Show More

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Abstract:

This paper reviews basics, similarities, and applications of two well-known biology inspired approximate data representation modalities: stochastic data representation and fuzzy linguistic variables.
Published in: 2007 IEEE International Symposium on Intelligent Signal Processing
Date of Conference: 03-05 October 2007
Date Added to IEEE Xplore: 08 February 2008
ISBN Information:
DOI: 10.1109/WISP.2007.4447532
Conference Location: Alcala de Henares, Spain
References is not available for this document.
Contents

I INTRODUCTION

Based on relativelysimple functional building blocks: neurons, biological organisms display remarkably robust all-around complex sensing, perception and adaptation behaviors that human-made computing and signal processing systems have still to match. The deciphering of the internal working mechanisms of biological systems is a continuous source of inspiration for the development of more reliable and intelligent computational and signal processingtechnologies.

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