DocumentCode
3108813
Title
Counting Boolean networks are universal approximators
Author
Tome, Jose A B
Author_Institution
IST, Lisbon Univ., Portugal
fYear
1998
fDate
20-21 Aug 1998
Firstpage
212
Lastpage
216
Abstract
A Boolean neural model is presented, where fuzzy reasoning emerges as a macroscopic property from individual neuron Boolean counting operations and random inter-neuron connections. The main objective of this work is to demonstrate that such networks are Universal Approximators. This is achieved through well known properties of non parametric techniques (Parzen Window estimators) to estimate any probability density function
Keywords
Boolean algebra; fuzzy neural nets; fuzzy set theory; inference mechanisms; probability; uncertainty handling; Boolean neural model; Parzen Window estimators; counting Boolean networks; fuzzy reasoning; macroscopic property; neuron Boolean counting operations; non parametric techniques; probability density function; random inter-neuron connections; universal approximators; Boolean functions; Flip-flops; Fuzzy neural networks; Fuzzy reasoning; Fuzzy sets; Fuzzy systems; Neural networks; Neurons; Probability density function; Production;
fLanguage
English
Publisher
ieee
Conference_Titel
Fuzzy Information Processing Society - NAFIPS, 1998 Conference of the North American
Conference_Location
Pensacola Beach, FL
Print_ISBN
0-7803-4453-7
Type
conf
DOI
10.1109/NAFIPS.1998.715567
Filename
715567
Link To Document