AJAS Cover
Frequency: Continuous
ISSN: 1546-9239 (Print)
ISSN: 1554-3641 (Online)
Research Article Open Access

VHDL DESIGN AND HARDWARE REALIZATION OF HYBRIDARTIFICIAL INTELLIGENCE ARCHITECTURE

Rajeswaran Nagalingam1, Tenneti Madhu2 and Munagala Suryakalavathi3
  • 1 Department of ECE, SNS College of Technolgoy, Coimbatore, Tamilnadu, India
  • 2 Principal, Swarnandhra Institute of Engg. and Tech., Narasapur Andhrapradesh, India
  • 3 Department of EEE, Jawaharlal Nehru Technological University, Hyderabad Andhrapradesh, India

Abstract

Evolutionary Algorithms (EA) use Genetic Algorithm (GA) in many optimization problems to efficiently compute the function value in less time. In this study the weight optimization of the Artificial Neural Net-work (ANN), using the Back Propagation Network (BPN), is tested and presented with GA. The combined architecture of Neuro-Genetic (Hybrid Artificial Intelligence) approach is proposed and simulated results are provided along with device Utilization, Simulation time, Timing analysis and power analysis by using very high speed integrated circuits Hardware Description Language (HDL).

American Journal of Applied Sciences
Volume 11 No. 5, 2014, 782-788

DOI: https://doi.org/10.3844/ajassp.2014.782.788

Submitted On: 19 October 2013 Published On: 24 February 2014

How to Cite: Nagalingam, R., Madhu, T. & Suryakalavathi, M. (2014). VHDL DESIGN AND HARDWARE REALIZATION OF HYBRIDARTIFICIAL INTELLIGENCE ARCHITECTURE. American Journal of Applied Sciences, 11(5), 782-788. https://doi.org/10.3844/ajassp.2014.782.788

Open AccessCopyright: © 2014 Rajeswaran Nagalingam, Tenneti Madhu and Munagala Suryakalavathi. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

  • 3,103 Views
  • 2,705 Downloads
  • 0 Citations

Download

Keywords

  • ANN
  • Evolutionary Algorithm
  • GA
  • Hybrid AI
  • VHDL