Abstract
Several parallel neural network (PNN) architectures are presented in this paper. PNNs can work parallelly and coordinately. The implementation of their training is much easier than that of a single NN. And there are many other attractive characteristics of PNNs such as a modular structure, easy implementation by hardware, high efficiency for their parallel structures (compared with sequential NN architectures), easy implementation of additional learning, etc. PNNs can be used to deal with such problems as data processing, pattern recognition, and classification. The learning and additional learning algorithms for PNNs are presented in this paper. Some simulation results are given to illustrate the advantages of all the PNNs considered.
Sign-in/Register to access full text options 
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 callDisclaimer: 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.
