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).
Highlights
INTRODUCTIONGenetic algorithms are a class of optimization procedures which broadly classified into three categories
The motivation for the development of neural network medicine, business and finance, robotic control, signal processing, computer vision and many other problems technology stemmed from the desire to develop an artificial system that could perform “intelligent” tasks that fall under the category of pattern recognition (Alsmadi et al, 2011)
The optimum selection of weights reduces the search-pace of the Genetic Algorithm (GA) to improve its performance (Kannaiah et al, 2011; Ditthakit and Chinnarasri, 2011)
Summary
Genetic algorithms are a class of optimization procedures which broadly classified into three categories. Ingo Rechenberg and Hans-Paul Schwefel (1960s and early 1970s) solved complex engineering problems through artificial evolution strategies using optimization optimum. They are well suited to the problem of training feed for-ward networks (Montana and Davis, 1989). A neural network is a Gate Array (FPGA) are becoming increasingly powerful data modeling tool that is able to capture and popular for prototyping and designing complex represent the complex input/output relation-ships hardware systems. Rajeswaran Nagalingam et al / American Journal of Applied Sciences 11 (5): 782-788, 2014 of implementation and reprogramming of FPGA’s in comparison with the custom VLSI technology offer attractive features for the designer (Botros and AbdulArir, 1994; Ameur et al, 2012; Mahyuddin et al, 2009) Here in this study, the neuro-Genetic system is designed and simulated by using VHDL
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