Sami I. Eddi

Work place: University of Technology/ Department of Computer Science and Information Systems, Baghdad, 10066, Iraq



Research Interests: Computer systems and computational processes, Neural Networks, Computer Networks, Theoretical Computer Science


Sami Eddi is an associated prof. in the University of Technology in Iraq. He completed his MSc degree in Al-Rafidain college of Sciences. His MSc and PhD degrees were from the University of Technology, Iraq. His research interests are computer networking, neural nets, and expert systems.

Author Articles
Coupling Perceptron Convergence Procedure with Modified Back-Propagation Techniques to Verify Combinational Circuits Design

By Raad F. Alwan Sami I. Eddi Baydaa Al-Hamadani

DOI:, Pub. Date: 8 May 2015

This paper proposed an algorithm for logic circuits verification using neural networks where a model is built to be trained and tested. The proposed algorithm for combinational circuits' verification is based on merging two of the well-known learning algorithms for neural networks. The first one is the Perceptron Convergence Procedure, which is used for learning the functions of the standard logic gates in order to simulate the whole circuit. While the second is a modified learning algorithm of Back-propagation neural networks to be used for the verification of the hardware design. The algorithm can predict the gates that cause the malfunction in the circuit design.
This work may be considered as a step toward building Distributed Computer Aided Design Environments depending on the parallel processing architecture, particularly in the Neurocomputer architecture.

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