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

This paper reviews basics, similarities, and applications of two well-known biology inspired approximate data representation modalities: stochastic data representation and fuzzy linguistic variables.

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.