Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
A Pattern Recognition System Using Evolvable Hardware
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
A Divide-and-Conquer Approach to Evolvable Hardware
ICES '98 Proceedings of the Second International Conference on Evolvable Systems: From Biology to Hardware
Journal of Cognitive Neuroscience
Proceedings of the Applications of Evolutionary Computing on EvoWorkshops 2002: EvoCOP, EvoIASP, EvoSTIM/EvoPLAN
Two-Step Incremental Evolution of a Prosthetic Hand Controller Based on Digital Logic Gates
ICES '01 Proceedings of the 4th International Conference on Evolvable Systems: From Biology to Hardware
A Comparison of Evolvable Hardware Architectures for Classification Tasks
ICES '08 Proceedings of the 8th international conference on Evolvable Systems: From Biology to Hardware
An Online EHW Pattern Recognition System Applied to Face Image Recognition
Proceedings of the 2007 EvoWorkshops 2007 on EvoCoMnet, EvoFIN, EvoIASP,EvoINTERACTION, EvoMUSART, EvoSTOC and EvoTransLog: Applications of Evolutionary Computing
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In this paper, we propose a new logic circuit design methodology for pattern recognition chips using the genetic algorithms. In the proposed design methodology, pattern data are transformed into the truth tables and the truth tables are generalized to adapt the unknown pattern data. The genetic algorithm is used to choose the generalization operators. The generalized, or evolved truth tables are then synthesized to logic circuits. Because of this data direct implementation approach, no floating point numerical circuits are required and the intrinsic parallelism in the data is embedded into the circuits. Consequently, high speed recognition systems can be realized with acceptable small circuit size. We have applied this methodology to the face image recognition and the sonar spectrum recognition tasks, and implemented them onto the developed FPGA-based reconfigurable pattern recognition board. The developed system demonstrates high recognition accuracy and much higher processing speed than the conventional approaches.