Evolvable Hardware (EHW) has been proposed as a new method for designing systems for real-world applications. In this paper it is applied for evolving a prosthetic hand controller. It is shown that better generalization performance than neural networks can be obtained. The proposed architecture is based on digital logic gates and its configuration is determined by two separate steps of evolution.
Josef Kittler合作论文数Centre for Vision, Speech and Signal Processing, School of Computer Science and Electronic Engineering, University of Surrey9
T Kanade合作论文数8JM Kleinberg合作论文数8F Mattern合作论文数7JC Mitchell合作论文数6
Bernhard Steffen合作论文数Department of Computer Science, TU Dortmund University2Cp Rangan合作论文数1MY Vardi合作论文数1Pc Haddow合作论文数1J Kleinberg合作论文数1
Oscar Nierstrasz合作论文数Institute of Computer Science, Faculty of Science, University of Bern;feenk GmbH1
Allan Kardec Duailibe Barros Filho合作论文数Laboratory for Biological Information Processing, Universidade Federal do Maranhāo1
Madhu Sudan合作论文数John A. Paulson School of Engineering and Applied Sciences, Harvard University1
Demetri Terzopoulos合作论文数Computer Graphics & Vision Laboratory, Department of Computer Science, Samueli School of Engineering, University of California, Los Angeles1
Christian Jutten合作论文数Departement of Data Science, Université Grenoble Alpes1
Moni Naor合作论文数Department of Computer Science and Applied Mathematics, Weizmann Institute of Science1Robert Meersman合作论文数University of Antwerp ; Semantics Technology and Applications Research Laboratory (STAR Lab)1
Gregory Hornby合作论文数University Affiliated Research Center (UARC);Evolvable Systems Group;NASA Ames Research Center;Intelligent Systems Division;University of California1
Tülay Adali合作论文数Machine Learning for Signal Processing Lab, Department of Computer Science and Electrical Engineering, College of Engineering and Information Technology, University of Maryland, Baltimore County1
