A fast computerized method for automatic simplification of boolean functions
Hazem Mokhtar El-Bakry, Nikos E. Mastorakis · International Conference on Systems · 2009
This paper introduces a new fast systematic method for minimization of the Boolean functions. This method is a very simple because there is no need for any visual representation such as Karnough map or arrangement technique such as Tabulation method and is very easy for programming. Furthermore, it is very suitable for boolean function with large number of variables (more than 4 variable). In adition, it is very simple for student's understanding. Moreover, the work presented in [23] is developed. In order to accelerate the operation of the proposed method, a new approach for fast term detection is presented. Such approach uses fast neural networks (FNNs) implemented in the frequency domain. The operation of these networks relies on performing cross correlation in the frequency domain rather than spatial one. It is proved mathematically and practically that the number of computation steps required for the presented FNNs is less than that needed by conventional neural networks (CNNs). Simulation results using MATLAB confirm the theoretical computations. Keywords— Boolean Functions, Simplification, FNNs, CNNs, Cross Correlation, Frequency Domain .