Model Inference with Reference Priors

We describe the application of model inference based on reference priors to two concrete examples in high energy physics: the determination of the CKM matrix parameters rhobar and etabar and the determination of the parameters m_0 and m_1/2 in a simplified version of the CMSSM SUSY model. We show how a 1-dimensional reference posterior can be mapped to the n-dimensional (n-D) parameter space of the given class of models, under a minimal set of conditions on the n-D function. This reference-based function can be used as a prior for the next iteration of inference, using Bayes' theorem recursively.Comment: Proceedings of PHYSTAT1

Robust Model Reference Adaptive Control of Angular Velocity Control Simulation of Brushed DC Motor

Electric motors play an important role in industry as well as our day-to-day life. They are used to generate electrical power in power plants and provide mechanical work in industries. They are also an indispensable part of our daily lives. Electric motors are very important pieces of equipment in our everyday lives. The brushed DC motor is considered to be basic electric motors. The aim of this paper is to introduce students to the modelling of brushed dc motor and to use computer simulation as a tool for conducting transient and control studies. Simulation can be very helpful in gaining insights to the dynamic behaviour and interactions that are often not readily apparent from reading theory. Next to having an actual system to experiment on, simulation is often chosen by engineers to study transient and control performance or to test conceptual designs. Presently, there are many control laws available to control the brushed dc motor. The control law of angular velocity depends on the motor parameters. The motor parameters are time varying, especially load torque, hence adaptive control is one of the best control law. In standard adaptive control, instability may be occur in the presence of unmodeled dynamics. Robust adaptive control is designed so the stability can be guaranteed

New Model Reference Adaptive System Speed Observer for Field-Oriented Control Induction Motor Drives Using Neural Networks

One of the primary advantages of field-oriented controlled induction motor for high performance application is the capability for easy field weakening and the full utilization of voltage and current rating of the inverter to obtain a wide dynamic speed rangeThis paper describes a Model Reference Adaptive System (MRAS) based scheme using Artificial Neural Network (ANN) for online speed estimation of sensorless vector controlled induction motor drive. The proposed MRAS speed observer uses the current model as an adaptive model. The neural network has been then designed and trained online by employing a back propagation network (BPN) algorithm. The estimator was designed and simulated in Matlab/Simulink. Simulation result shows a good performance of speed estimator. The simulation results show good performance in various operating conditions. Also Performance analysis of speed estimator with the change in resistances of stator is presented. Simulation results show this estimator robust to parameter variations especially resistances of stator

Background stratospheric aerosol reference model

In this analysis, a reference background stratospheric aerosol optical model is developed based on the nearly global SAGE 1 satellite observations in the non-volcanic period from March 1979 to February 1980. Zonally averaged profiles of the 1.0 micron aerosol extinction for the tropics and the mid- and high-altitudes for both hemispheres are obtained and presented in graphical and tabulated form for the different seasons. In addition, analytic expressions for these seasonal global zonal means, as well as the yearly global mean, are determined according to a third order polynomial fit to the vertical profile data set. This proposed background stratospheric aerosol model can be useful in modeling studies of stratospheric aerosols and for simulations of atmospheric radiative transfer and radiance calculations in atmospheric remote sensing

New Model Reference Adaptive System Speed Observer for Field-Oriented Control Induction Motor Drives Using Neural Networks

One of the primary advantages of field-oriented controlled induction motor for high performance application is the capability for easy field weakening and the full utilization of voltage and current rating of the inverter to obtain a wide dynamic speed rangeThis paper describes a Model Reference Adaptive System (MRAS) based scheme using Artificial Neural Network (ANN) for online speed estimation of sensorless vector controlled induction motor drive. The proposed MRAS speed observer uses the current model as an adaptive model. The neural network has been then designed and trained online by employing a back propagation network (BPN) algorithm. The estimator was designed and simulated in Matlab/Simulink. Simulation result shows a good performance of speed estimator. The simulation results show good performance in various operating conditions. Also Performance analysis of speed estimator with the change in resistances of stator is presented. Simulation results show this estimator robust to parameter variations especially resistances of stator

A Model of Reference-Dependent Preferences

We develop a model that fleshes out, extends, and modifies existing models of reference dependent preferences and loss aversion while accommodating most of the evidence motivating these models. Our approach makes reference-dependent theory more broadly applicable by avoiding some of the ways that prevailing models if applied literally and without ancillary assumptions make variously weak and incorrect predictions. Our model combines the reference-dependent gain-loss utility with standard economic 'consumption utility' and clarifies the relationship between the two. Most importantly, we posit that a person's reference point is her recent expectations about outcomes (rather than the status quo), and assume that behavior accords to a personal equilibrium: The person maximizes utility given her rational expectations about outcomes, where these expectations depend on her own anticipated behavior. We apply our theory to consumer behavior, and emphasize that a consumer's willingness to pay for a good is endogenously determined by the market distribution of prices and how she expects to respond to these prices. Because a buyer's willingness to buy depends on whether she anticipates buying the good, for a range of market prices there are multiple personal equilibria. This multiplicity disappears when the consumer is sufficiently uncertain about the price she will face. Because paying more than she anticipated induces a sense of loss in the buyer, the lower the prices at which she expects to buy the lower will be her willingness to pay. In some situations, a known stochastic decrease in prices can even lower the quantity demanded.

Revised reference model for nitric acid

A nearly global set of data on the nitric acid distribution was obtained for seven months by the Limb Infrared Monitor of the Stratosphere (LIMS) experiment on the Nimbus 7 spacecraft. The evaluation of the accuracy, precision, and resolution of these data is described, and a description of the major features of the nitric acid distributions is presented. The zonal mean for nitric acid is distributed in a stratospheric layer that peaks near 30 mb, with the largest mixing ratios occurring in polar regions, especially in winter