Abstract
A digital hardware implementation of Radial Basis Function Neural Network (RBFNN) based on sigma-delta modulated bit-streams is presented. Through the change of feedback coefficient in the framework of traditional sigma-delta modulator, a new limiting amplifier modulator (LAM) is fabricated, and the approximation to Gauss kernel function can be achieved by the combination of several LAMs with different coefficients. The bit-stream neurons with Gauss kernel function and a whole feed-forward artificial neural network is implemented on field programmable gate array (FPGA). Thus a nonlinear function approximation problem can be solved by the neural networks presented.
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.
