Algorithmic Architecture For Artificial Learning Procedures By Genetics

Ahmad Yusairi, Bani Hashim and Mohd Ariff , Mat Hanafiah (2005) Algorithmic Architecture For Artificial Learning Procedures By Genetics. Project Report. UTeM, Melaka, Malaysia. (Submitted)

PDF (Full Text) Algorithmic_Architecture_For_Artificial_Learning_Procedures_By_Genetics.pdf - Submitted Version Restricted to Registered users only Download (3MB)

Abstract

Algorithmic architecture is a design of procedures and steps that has a specific structure. Artificial learning, however, is a design of training that comprises of pre-defined objectives meant for computational activities. All of these activities follow the procedure found in biological genetics. Gene expression is the flow of genetic information from Deoxynucleic acid via Ribonucleic acid to protein. Using gene expression as a model, the algorithmic architecture and the artificial learning are designed to solve selected problems in control engineering. This work has produced methods to creating control system's transfer function responses. It is found that transfer function responses are indeed dependent upon specified gene expression's characteristics. In addition, this work also has produced methods in representing discrete control circuits in a unique way that is readable and traceable for troubleshooting.

Actions (login required)

View Item