Applicability of feed-forward and recurrent neural networks to Boolean function complexity modeling

BEG, A and CHANDANAPRASAD, P and BEG, A (2008) Applicability of feed-forward and recurrent neural networks to Boolean function complexity modeling. Expert Systems with Applications, 34 (4). pp. 2436-2443. ISSN 09574174

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Official URL: http://dx.doi.org/10.1016/j.eswa.2007.04.010

Abstract

In this paper, we present the feed-forward neural network (FFNN) and recurrent neural network (RNN) models for predicting Boolean function complexity (BFC). In order to acquire the training data for the neural networks (NNs), we conducted experiments for a large number of randomly generated single output Boolean functions (BFs) and derived the simulated graphs for number of min-terms against the BFC for different number of variables. For NN model (NNM) development, we looked at three data transformation techniques for pre-processing the NN-training and validation data. The trained NNMs are used for complexity estimation for the Boolean logic expressions with a given number of variables and sum of products (SOP) terms. Both FFNNs and RNNs were evaluated against the ISCAS benchmark results. Our FFNNs and RNNs were able to predict the BFC with correlations of 0.811 and 0.629 with the benchmark results, respectively. (c) 2007 Elsevier Ltd. All rights reserved.

Item Type: Article
Subjects: T Technology > T Technology (General)
Q Science > QA Mathematics > QA75.5-76.95 Electronic computers. Computer science
Divisions: Faculty of Engineering and Technology (FET)
Depositing User: Ms Suzilawati Abu Samah
Date Deposited: 08 Sep 2011 02:05
Last Modified: 13 Feb 2014 03:01
URI: http://shdl.mmu.edu.my/id/eprint/2670

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