Abstract
Resistive-type distributed neural networks (DNNs) provide a self-scaling structure for the neuron, which can spontaneously adapt itself to different numbers of inputs. In lumped neural networks, the neuron should be changed whenever the number of inputs changes due to the applications; redesigning the neuron is not practical, particularly for hardware implementations. In this brief, a group of feedforward DNNs based on a continuous valued number system is proposed, which outperforms not only the lumped neural networks but also the conventional DNNs because of the reduced sensitivity to noise.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: IEEE Transactions on Circuits and Systems II: Express Briefs
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
