Adaptively Biasing the Weights of Adaptive Filters

Dynamic-range analysis and maximization of micropower G[subscript m]-C bandpass filters by adaptive biasing

We analyze and present an input gain-varying scheme for maximizing dynamic range in a well-known Gm–C bandpass filter by both minimizing noise for small input signals, and by achieving balanced swing levels at all filter nodes for large input signals. A micropower bandpass filter suitable for use in cochlear implants and other powerconstrained biomedical applications was implemented and tested ...

Dynamic-range Analysis and Maximization of Micropower Gm-C Bandpass Filters by Adaptive Biasing

We analyze and present an input gain-varying scheme for maximizing dynamic range in a well-known Gm–C bandpass filter by both minimizing noise for small input signals, and by achieving balanced swing levels at all filter nodes for large input signals. A micropower bandpass filter suitable for use in cochlear implants and other powerconstrained biomedical applications was implemented and tested ...

Reduction of Photodiode Nonlinearities by Adaptive Biasing

Learning adaptive leg cycles using fitness biasing

This paper discusses the use of fitness biasing to alter the control of a seven microprocessor robot as it shifts from one environment to another. The robot was initially using a gait evolved to work on a smooth surface (tile). When tested on a rough surface (carpet) the learned gait was found to be inappropriate because the legs were causing drag as they repositioned. An efficient move to repo...

Smoothed Biasing Forces Yield Unbiased Free Energies with the Extended-System Adaptive Biasing Force Method

We report a theoretical description and numerical tests of the extended-system adaptive biasing force method (eABF), together with an unbiased estimator of the free energy surface from eABF dynamics. Whereas the original ABF approach uses its running estimate of the free energy gradient as the adaptive biasing force, eABF is built on the idea that the exact free energy gradient is not necessary...