Nonlinear identification and control of muscle relaxant dynamics.

The work reported in this thesis comprised two major parts which are: 1) Off-line nonlinear identification of muscle relaxant dynamics, 2) Simulation-based design of a variety of controllers (ranging from classical PID to nonlinear self-tuners) for the closed-loop control of muscle relaxation. Relaxant drugs namely, Vecuronium and Atracurium are considered throughout. Off-line identification studies, using two special nonlinear identification packages (Nonlinear Identification package and Nonlinear Orthogonal Identification package), were carried out to determine nonlinear difference equation models (NARMAX) that best fit (in the least squares sense) recorded data from trials on humans and dogs for each drug. After validation, these models were assumed to represent, in a nonlinear polynomial form, the muscle relaxant drugs pharmacology. Two different approaches were explored for determining the physiological structure of both relaxant drugs: a) The drug model to comprise a pharmacokinetics part to represent the drug distribution, and pharmacodynamics which are often modelled by using the well known Hill equation. b) A cross-correlation approach based on Volterra series. With the relaxant dynamics structure thus fixed, the work proceeded to the control phase. Simple three-term PID controllers were first designed with their parameters being optimised, off-line, using the Simplex method. The non-adaptive nature of this class of controllers makes their robustness open to question when the system parameters for which they have been optimised change. Hence adaptive controllers in the form of linear and nonlinear generalised minimum variance, self-tuners, generalised predictive and nonlinear k-step ahead predictive controllers were also considered. All these latter control approaches are shown to be satisfactory, in terms of transient and steady state performance

Intelligent Sensor Networks

In the last decade, wireless or wired sensor networks have attracted much attention. However, most designs target general sensor network issues including protocol stack (routing, MAC, etc.) and security issues. This book focuses on the close integration of sensing, networking, and smart signal processing via machine learning. Based on their world-class research, the authors present the fundamentals of intelligent sensor networks. They cover sensing and sampling, distributed signal processing, and intelligent signal learning. In addition, they present cutting-edge research results from leading experts

On the use of 'improved' estimators in econometrics

This thesis carries a title that might appear to be too extensive as a topic. However, those familiar with the literature on biased estimators may agree that there is a well defined class of estimation procedures of interest to both mathematical statisticians and econometricians. Efforts to introduce ideas which deviate from the traditional classical notion of unbiasedness have encountered enormous resistance. Admittedly, results relating to biased estimators are not as wellestablished as those relating to unbiased estimators, but unbiasedness is an arbitrary and unnecessarily stringent criterion. One should not therefore neglect the usefulness of biased estimators. With this background in mind, the thesis was written to synthesize the many differently motivated contributions which aim at improved estimation of unknown economic linear relationships. Apart from highlighting the author’s own contributions in the area, the author has also attempted to make the thesis a self-contained one. Chapter 1 motivates the study and defines the framework in which new estimators are developed. The fundamentals of Bayesian inference are discussed and the relation between formal and empirical Bayes procedures is examined. Chapter 2 provides a synthesis of different attempts to improve upon the traditional unbiased estimator. This chapter is necessary because it is not generally acknowledged that the differently motivated efforts can lead to the same result - namely, some sort of shrinkage must be introduced to improve estimation and that all the improved estimators are basically generalised Bayes rules. Chapter 3 introduces the controversial ridge estimator and provides a comprehensive survey. A new contribution made in this chapter is the introduction of a recursive algorithm for generating the ridge trace. Chapter 4, 5 and 6 form the core of the thesis where new ideas are developed. Specifically, Chapter 4 attempts theoretical and Monte Carlo studies of the potential and realised reduction in risk of the biased estimators. A number of good adaptive ridge estimators are identified. As an illustration these are applied to re-estimating an investment function. Significantly more accurate predictions are achieved by the biased estimators than by conventional ordinary least squares estimator and the preliminary test estimators. Two new contributions are made in Chapter 5. Firstly, an analysis of seasonal variability in the distributed lag model sets the stage for the introduction of various estimators which can incorporate bi-dimensional prior information in the form of exchangeability and smoothness. Secondly, estimation of distributed lag model in the frequency domain is justified and the Spectral Ridge Estimator is introduced as an extension of Hannan’s Efficient Estimator. The estimator’s performance is compared to other well-known estimators using Almon’s data. Chapter 6 works out the small sample bias and mean square error of a Generalised Ridge Instrumental Variable estimator for a structural equation in the context of a simultaneous equation system. The problem of undersized sample is tackled and the traditional optimism about 2SPC questioned. A new estimator which involves the application of ridge regression instead of the traditional least square regression at both stages of a 2SLS procedure is proposed and its statistical properties analysed (both asymptotically and in finite sample). Some further results concerning ridge regression are presented in the last chapter, i.e. 7. The robustness of ridge regression under misspecification is analysed. Problems of testing stochastic hypotheses and the construction of confidence sets are also discussed. Some of the criticisms of the technique are reviewed and a personal view is expressed

An econometric analysis of the forward freight market

The success or failure of a derivatives (futures or forward) contract is determined by its ability to perform its economic functions efficiently, and therefore, to provide benefits to economic agents, over and above the benefits they derive from the spot market. These economic functions are price discovery and risk management through hedging. A considerable amount of empirical research has been directed towards examining these functions in different financial and commodity derivatives markets. The evidence however, on the over-the-counter FFA market is very limited. This thesis therefore, by investigating these issues provides new evidence in the literature for a forward market with some unique characteristics such as the trading of a service. Our empirical results can be summarised as follows. First, the FFA contracts perform their price discovery function efficiently since forward prices contribute to the discovery of new information regarding both current and expected spot prices. Furthermore, most FFA contracts contribute in the volatility of the relevant spot rate, and therefore, further support the notion of price discovery. Second, the introduction of FFA contracts has not had a detrimental effect on the volatility of the underlying spot market. On the contrary, it appears that there has been an improvement in the way that news is transmitted into prices following the onset of FFA trading. Third, FFA prices fail to reduce market risk to the extent evidenced in other markets in the literature and, hence, the FFA market does not perform its risk management function satisfactorily; this is thought to be the result of the lack of the cost-of-carry arbitrage relationship of storable assets that keeps spot and derivatives prices close together. Fourth, there seems to be a positive relationship between bid-ask spreads and expected price volatility in most FFA trading routes. Finally, in the routes where the cointegrating vector is restricted to be the lagged basis, the VECM generates more accurate forecasts than the VAR model and in the routes where the cointegrating vector is not restricted to be the lagged basis the VAR generates more accurate forecasts than the VECM model