On the filtering properties of evolved gate arrays
- 20 January 2003
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
Abstract
A small gate array is evolved extrinsically to carry out a low pass filtering task defined over fifteen different frequencies. The circuit is evolved by assessing its response to digitised sine waves. Two different fitness functions are contrasted. One is based on computing the sum of the absolute differences between the actual response and that desired, the other is defined by examining characteristics of the discrete Fourier transform of the output. The gate arrays possess some linear properties, which means that they are capable of filtering composite signals which have not been encountered in training. This includes signals with noise added and with frequencies which are not in the training set.Keywords
This publication has 10 references indexed in Scilit:
- Aspects of digital evolution: Geometry and learningPublished by Springer Nature ,1998
- On the automatic design of robust electronics through artificial evolutionPublished by Springer Nature ,1998
- Aspects of digital evolution: Evolvability and architecturePublished by Springer Nature ,1998
- Design of low complexity FIR filters using genetic algorithms and directed graphsPublished by Institution of Engineering and Technology (IET) ,1997
- An evolved circuit, intrinsic in silicon, entwined with physicsPublished by Springer Nature ,1997
- Machine learning approach to gate-level Evolvable HardwarePublished by Springer Nature ,1997
- Gaining insight into evolutionary programming through landscape visualization: An investigation into IIR filteringPublished by Springer Nature ,1997
- Genetic algorithm implementation of stack filter design for image restorationIEE Proceedings - Vision, Image, and Signal Processing, 1996
- Use of minimum-adder multiplier blocks in FIR digital filtersIEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing, 1995
- Multiplier-less FIR filter design using a genetic algorithmIEE Proceedings - Vision, Image, and Signal Processing, 1994