Abstract
Several practical applications are concerned with the identification of the least squares (LS) solution. The objective is to attain this solution accurately and efficiently while conserving resources. The computational and storage requirements to determine the LS solution by any iterative procedure become prohibitively large as the problem dimensions grow. This work presents some architectures based on thresholded binary networks which recover regularized LS solutions by partitioning such networks and adopting a switching operation between active and inactive partitions to optimize the objective function. Also, an iterative method based on steepest descent is briefly discussed and implemented. It yields reliable estimates of the regularized LS solution, while providing savings in computation and storage.
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 callMore From: IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing
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.
