Abstract
A recurrent neural network is developed for robust optimal filter design. The purpose is to fill the gap between the real-time computation requirement in practice and the computational complexity of the filter design in the case that the statistical properties of noise are unknown. First, an H/sub /spl infin// requirement and an L/sub 2/ requirement of the filter design problem are formulated as a group of linear matrix inequalities. On this basis, an optimization problem is introduced to solve the robust optimal filter design problem. Then, a recurrent neural network is deliberately developed for solving the optimization problem in real time. The effectiveness and efficiency of the recurrent neural network is shown by use of theoretical and simulation results.
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 I: Fundamental Theory and Applications
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.
