The problem considered is the design of a feedback system containing a linear, time-invariant, minimum-phase plant, whose parameters are known only within given bounds, such that the time-response of the system remains within specified limits. A quasi-optimal design, for given design constraints, is one which minimizes the effect of white sensor-noise on the input to the plant. Horowitz and Sid...

The leaky integrator, the basis for many neuronal models, possesses a negative group delay when a time-delayed recurrent inhibition is added to it. By means of this delay, the leaky integrator becomes a predictor for some frequency components of the input signal. The prediction properties are derived analytically, and an application to a local field potential is provided.

This article studies 2-D formation stabilization and maneuvering of mobile agents governed by double-integrator dynamics. The desired is described a set triple-agent interior angles. A carefully chosen such angle constraints guarantees that the rigid. To achieve rigid formation, control law proposed using only local velocity direction measurements. We show closed-loop dynamics when each agent m...

OpenTuner can help users achieve better or more portable performance in their speci c domain through program autotuning. A key challenge for users seeking good autotuning performance in OpenTuner is selecting a search approach appropriate for problem. However, not only are current in-situ learning search approaches not robust enough to handle all search spaces, but there are also too many possi...

Introduction: The leaky integrator is a key subsystem in pulse-mode artificial neuron implementations [1]. In most implementations reported to date this function has been implemented with the explicit use of integrated capacitors and with fixed time constants. More recently it has been noted that mimicking real biological systems is better accomplished if the integrator time constants are adapt...

OpenTuner can help users achieve better or more portable performance in their speci c domain through program autotuning. A key challenge for users seeking good autotuning performance in OpenTuner is selecting a search approach appropriate for problem. However, not only are current in-situ learning search approaches not robust enough to handle all search spaces, but there are also too many possi...

The process of empirical autotuning results in the generation of many code variants which are tested, found to be suboptimal, and discarded. By retaining annotated performance profiles of each variant tested over the course of many autotuning runs of the same code across different hardware environments and different input datasets, we can apply machine learning algorithms to generate classifier...