Controller performance design and assessment using nonlinear generalized minimum variance benchmark : scalar case

A nonlinear version of the Generalized Minimum Variance (GMV) multivariable control law has been recently derived for the control of nonlinear, possibly time-varying systems. This paper presents the results of the controller performance assessment against this Nonlinear GMV controller in the scalar case. The minimum variance of the generalized output is estimated from routine operating data given only the plant time delay and the technique is applied to a nonlinear reactor control example

A Model for the Genesis of Arterial Pressure Mayer Waves from Heart Rate and Sympathetic Activity

Both theoretic models and cross-spectral analyses suggest that an oscillating sympathetic nervous outflow generates the low frequency arterial pressure fluctuations termed Mayer waves. Fluctuations in heart rate also have been suggested to relate closely to Mayer waves, but empiric models have not assessed the joint causative influences of hemt rate and sympathetic activity. Therefore, we constructed a model based simply upon the hemodynamic equation deriving from Ohm's Law. With this model, we determined time relations and relative contributions of heart rate and sympathetic activity to the genesis of arterial pressure Mayer waves. We assessed data from eight healthy young volunteers in the basal state and in a high sympathetic state known to produce concurrent increases in sympathetic nervous outflow and Mayer wave amplitude. We fit the Mayer waves (0.05-0.20 Hz) in mean arterial pressure by the weighted sum ofleading oscillations in heart rate and sympathetic nerve activity. This model of our data showed heart rate oscillations leading by 2-3.75 seconds were responsible for almost half of the variance in arterial pressure (basal R^2=0.435±0.140, high sympathetic R^2=0.438±0.180). Surprisingly, sympathetic activity (lead 0-5 seconds) contributed only modestly to the explained variance in Mayer waves during either sympathetic state (basal: ∆R^2=0.046±0.026; heightened: ∆R^2=0.085±0.036). Thus, it appears that heart rate oscillations contribute to Mayer waves in a simple linear fashion, whereas sympathetic fluctuations contribute little to Mayer waves in this way. Although these results do not exclude an important vascular sympathetic role, they do suggest that additional Ji1ctors, such as sympathetic transduction into vascular resistance, modulate its influence.Binda and Fred Shuman Foundation; National Institute on Aging (AG14376)

A Model for the Genesis of Arterial Pressure Mayer Waves from Heart Rate and Sympathetic Activity

Both theoretic models and cross-spectral analyses suggest that an oscillating sympathetic nervous outflow generates the low frequency arterial pressure fluctuations termed Mayer waves. Fluctuations in heart rate also have been suggested to relate closely to Mayer waves, but empiric models have not assessed the joint causative influences of hemt rate and sympathetic activity. Therefore, we constructed a model based simply upon the hemodynamic equation deriving from Ohm's Law. With this model, we determined time relations and relative contributions of heart rate and sympathetic activity to the genesis of arterial pressure Mayer waves. We assessed data from eight healthy young volunteers in the basal state and in a high sympathetic state known to produce concurrent increases in sympathetic nervous outflow and Mayer wave amplitude. We fit the Mayer waves (0.05-0.20 Hz) in mean arterial pressure by the weighted sum ofleading oscillations in heart rate and sympathetic nerve activity. This model of our data showed heart rate oscillations leading by 2-3.75 seconds were responsible for almost half of the variance in arterial pressure (basal R^2=0.435±0.140, high sympathetic R^2=0.438±0.180). Surprisingly, sympathetic activity (lead 0-5 seconds) contributed only modestly to the explained variance in Mayer waves during either sympathetic state (basal: ∆R^2=0.046±0.026; heightened: ∆R^2=0.085±0.036). Thus, it appears that heart rate oscillations contribute to Mayer waves in a simple linear fashion, whereas sympathetic fluctuations contribute little to Mayer waves in this way. Although these results do not exclude an important vascular sympathetic role, they do suggest that additional Ji1ctors, such as sympathetic transduction into vascular resistance, modulate its influence.Binda and Fred Shuman Foundation; National Institute on Aging (AG14376)

A flexible approach to parametric inference in nonlinear time series models

Many structural break and regime-switching models have been used with macroeconomic and …nancial data. In this paper, we develop an extremely flexible parametric model which can accommodate virtually any of these speci…cations and does so in a simple way which allows for straightforward Bayesian inference. The basic idea underlying our model is that it adds two simple concepts to a standard state space framework. These ideas are ordering and distance. By ordering the data in various ways, we can accommodate a wide variety of nonlinear time series models, including those with regime-switching and structural breaks. By allowing the state equation variances to depend on the distance between observations, the parameters can evolve in a wide variety of ways, allowing for everything from models exhibiting abrupt change (e.g. threshold autoregressive models or standard structural break models) to those which allow for a gradual evolution of parameters (e.g. smooth transition autoregressive models or time varying parameter models). We show how our model will (approximately) nest virtually every popular model in the regime-switching and structural break literatures. Bayesian econometric methods for inference in this model are developed. Because we stay within a state space framework, these methods are relatively straightforward, drawing on the existing literature. We use arti…cial data to show the advantages of our approach, before providing two empirical illustrations involving the modeling of real GDP growth

Attention-dependent modulation of cortical taste circuits revealed by granger causality with signal-dependent noise

We show, for the first time, that in cortical areas, for example the insular, orbitofrontal, and lateral prefrontal cortex, there is signal-dependent noise in the fMRI blood-oxygen level dependent (BOLD) time series, with the variance of the noise increasing approximately linearly with the square of the signal. Classical Granger causal models are based on autoregressive models with time invariant covariance structure, and thus do not take this signal-dependent noise into account. To address this limitation, here we describe a Granger causal model with signal-dependent noise, and a novel, likelihood ratio test for causal inferences. We apply this approach to the data from an fMRI study to investigate the source of the top-down attentional control of taste intensity and taste pleasantness processing. The Granger causality with signal-dependent noise analysis reveals effects not identified by classical Granger causal analysis. In particular, there is a top-down effect from the posterior lateral prefrontal cortex to the insular taste cortex during attention to intensity but not to pleasantness, and there is a top-down effect from the anterior and posterior lateral prefrontal cortex to the orbitofrontal cortex during attention to pleasantness but not to intensity. In addition, there is stronger forward effective connectivity from the insular taste cortex to the orbitofrontal cortex during attention to pleasantness than during attention to intensity. These findings indicate the importance of explicitly modeling signal-dependent noise in functional neuroimaging, and reveal some of the processes involved in a biased activation theory of selective attention

Mapping the epileptic brain with EEG dynamical connectivity: established methods and novel approaches

Several algorithms rooted in statistical physics, mathematics and machine learning are used to analyze neuroimaging data from patients suffering from epilepsy, with the main goals of localizing the brain region where the seizure originates from and of detecting upcoming seizure activity in order to trigger therapeutic neurostimulation devices. Some of these methods explore the dynamical connections between brain regions, exploiting the high temporal resolution of the electroencephalographic signals recorded at the scalp or directly from the cortical surface or in deeper brain areas. In this paper we describe this specific class of algorithms and their clinical application, by reviewing the state of the art and reporting their application on EEG data from an epileptic patient