Abstract
This study presents the development of a synthesizable VHDL (very high speed integrated circuit hardware description language) model of a general regression neural network (GRNN). The GRNN has a four-layer structure which is comprised of an input layer, a pattern layer, a summation layer and an output layer. The designed system can be used for pattern classification applications. Iris dataset is used to test the GRNN in this study. Simulation results show that pattern classification by digital implementation of GRNN has successfully achieved.
Keywords
VHDL
Hardware description language
Computer science
Layer (electronics)
Artificial neural network
Computer architecture
Artificial intelligence
Regression
Computer hardware
Pattern recognition (psychology)
Machine learning
Field-programmable gate array
Subject Areas
Neural Networks and Applications
·Artificial Intelligence ·Physical Sciences
Fault Detection and Control Systems
·Control and Systems Engineering ·Physical Sciences
CCD and CMOS Imaging Sensors
·Electrical and Electronic Engineering ·Physical Sciences
Citations by Year
2014: 1
Loading…
Loading…