Netlab MLP - Performance Evaluation for Pattern Recognition in Myoletric Signal | Zendy

Gabriel Cirac | Zendy; Robson Luiz Moreno | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Netlab MLP - Performance Evaluation for Pattern Recognition in Myoletric Signal

Author(s) -

Gabriel Cirac,

Robson Luiz Moreno

Publication year - 2018

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2018.04.092

Subject(s) - computer science , pattern recognition (psychology) , classifier (uml) , resampling , artificial intelligence , signal (programming language) , speech recognition , machine learning , data mining , programming language

The high accuracy and the shortest time in the recognition of myoelectric signal patterns are essential requirements for the development of artificial limbs. In this study the approach to training optimization and pattern recognition - Netlab MLP - is evaluated in different scenarios, in order to provide an optimal configuration. In addition, the resampling of points is done in order to reduce and generalize the amount of information that arrives at the MLP classifier, to verify if the algorithm works well when fed by a reduced set of data.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research