Randomized Improved Correlation Matrix Memory In ANN-Based Semi-Autonomous Robotic System

Abstract

This paper presents the development of a semi-autonomous robotic system that operates based on Artificial Neural Networks (ANN). The ANN is a computational technique which models the way biological neurons work. ANN is used in pattern recognition, adaptive filtering, control systemand others. Application of embedded systems technology such as microcontroller or programmable logic device (PLD) is necessary to control the system. Through wireless communication between the embedded system and a general-purpose computer, the system can be further empowered.With regard to the principle of equivalence of hardware and software, ANN can be developed in software or hardware form. The system is a prototype that serves as a foundation for further development for many applications including space exploration and services. An innovative improved version of Correlation Matrix Memory (CMM) learning algorithm capable of computing random optimized memory matrix and solving linearly inseparable exclusive or (XOR) logical operations was discovered and implemented. It is termed as Randomized Improved Correlation Marix Memory (RICMM).