43,916 research outputs found
Yield-driven power-delay-optimal CMOS full-adder design complying with automotive product specifications of PVT variations and NBTI degradations
We present the detailed results of the application of mathematical optimization algorithms to transistor sizing in a full-adder cell design, to obtain the maximum expected fabrication yield. The approach takes into account all the fabrication process parameter variations specified in an industrial PDK, in addition to operating condition range and NBTI aging. The final design solutions present transistor sizing, which depart from intuitive transistor sizing criteria and show dramatic yield improvements, which have been verified by Monte Carlo SPICE analysis
Modeling Financial Time Series with Artificial Neural Networks
Financial time series convey the decisions and actions of a population of human actors over time. Econometric and regressive models have been developed in the past decades for analyzing these time series. More recently, biologically inspired artificial neural network models have been shown to overcome some of the main challenges of traditional techniques by better exploiting the non-linear, non-stationary, and oscillatory nature of noisy, chaotic human interactions. This review paper explores the options, benefits, and weaknesses of the various forms of artificial neural networks as compared with regression techniques in the field of financial time series analysis.CELEST, a National Science Foundation Science of Learning Center (SBE-0354378); SyNAPSE program of the Defense Advanced Research Project Agency (HR001109-03-0001
Parametric, nonparametric and parametric modelling of a chaotic circuit time series
The determination of a differential equation underlying a measured time
series is a frequently arising task in nonlinear time series analysis. In the
validation of a proposed model one often faces the dilemma that it is hard to
decide whether possible discrepancies between the time series and model output
are caused by an inappropriate model or by bad estimates of parameters in a
correct type of model, or both. We propose a combination of parametric
modelling based on Bock's multiple shooting algorithm and nonparametric
modelling based on optimal transformations as a strategy to test proposed
models and if rejected suggest and test new ones. We exemplify this strategy on
an experimental time series from a chaotic circuit where we obtain an extremely
accurate reconstruction of the observed attractor.Comment: 19 pages, 8 Fig
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