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» On a quasi-ordering on Boolean functions
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ASPDAC
2007
ACM
158views Hardware» more  ASPDAC 2007»
13 years 11 months ago
Symbolic Model Checking of Analog/Mixed-Signal Circuits
This paper presents a Boolean based symbolic model checking algorithm for the verification of analog/mixedsignal (AMS) circuits. The systems are modeled in VHDL-AMS, a hardware des...
David Walter, Scott Little, Nicholas Seegmiller, C...
DAM
2008
102views more  DAM 2008»
13 years 7 months ago
Formulas for approximating pseudo-Boolean random variables
We consider {0, 1}n as a sample space with a probability measure on it, thus making pseudo-Boolean functions into random variables. We then derive explicit formulas for approximat...
Guoli Ding, Robert F. Lax, Jianhua Chen, Peter P. ...
GECCO
2009
Springer
122views Optimization» more  GECCO 2009»
13 years 11 months ago
Visualising random boolean network dynamics
We propose a simple approach to visualising the time behaviour of Random Boolean Networks (RBNs), and demonstrate the approach by examining the effect of canalising functions for ...
Susan Stepney
ASPDAC
2005
ACM
117views Hardware» more  ASPDAC 2005»
14 years 1 months ago
Dynamic symmetry-breaking for improved Boolean optimization
With impressive progress in Boolean Satisfiability (SAT) solving and several extensions to pseudo-Boolean (PB) constraints, many applications that use SAT, such as highperformanc...
Fadi A. Aloul, Arathi Ramani, Igor L. Markov, Kare...
CORR
2010
Springer
88views Education» more  CORR 2010»
13 years 7 months ago
A fuzzified BRAIN algorithm for learning DNF from incomplete data
Aim of this paper is to address the problem of learning Boolean functions from training data with missing values. We present an extension of the BRAIN algorithm, called U-BRAIN (U...
Salvatore Rampone, Ciro Russo