British Journal of Mathematics & Computer Science 3(2): 135-152, 2013 SCIENCEDOMAIN international www.sciencedomain.org ________________________________________________________________ _____________________________________ *Corresponding author: skadry@gmail.com; On the Improvement of Multi-nary Content Addressable Memory Ahmad Abboud 1 , Ali Kalakech 1 , Seifedine Kadry 1* and Ibrahim Sayed 1 1 Arts, Sciences and Technologies University, Lebanon. Received: 22 November 2012 Accepted: 6 February 2013 Published: 13 March 2013 _______________________________________________________________________ Abstract Aims: Using Simple Artificial Neural Networks, and away from strict Boolean logic, this paper proposes a new design of memory array that has the ability to recognize erroneous and deformed data and specify the rate of error. Methodology: To achieve this work, artificial neural network was exploited to be the actor responsible of representing the crude of the building. It’s worth mentioning that simple neurons with binary step function and identity function were used, which will facilitate the way of implementation. The connection of few neurons in a simple network issues an exclusive X gate, which accepts only one value X (where X ∊ℝ+) with an acceptable error rate α. This gate will be the main core of designing a memory cell that can learn a value X and recognized this value when requested. Results: After several stages of development, the final version of this memory cell will serve as a node unit of a large memory array which can recognize a data word or even a whole image with the ability to accept and recognize distorted data. Specific software that simulates the designed networks was developed in order to declare the efficiency of this memory. The obtained result will judge the Network. Keywords: Neural network, binary step function, identity function, Content addressable memory (CAM). 1 Introduction The simplest is the unit, the more complex it can achieve. This is the key behind the dominance of digital systems worldwide. So, what about human brains, that great complex system which can store, recognize and solve complex problem. The construction unit of this system is the neuron. But how can this simple unit build like this complex system? And can we benefit from this concept in order to upgrade our artificial systems? Many studies have been conducted on artificial neural network. After the discovery of back Research Article