There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Deposits of clastic carbonate-dominated (calciclastic) sedimentary slope systems in the rock record have been identified mostly as linearly-consistent carbonate apron deposits, even though most ancient clastic carbonate slope deposits fit the submarine fan systems better. Calciclastic submarine fans are consequently rarely described and are poorly understood. Subsequently, very little is known especially in mud-dominated calciclastic submarine fan systems. Presented in this study are a sedimentological core and petrographic characterisation of samples from eleven boreholes from the Lower Carboniferous of Bowland Basin (Northwest England) that reveals a >250 m thick calciturbidite complex deposited in a calciclastic submarine fan setting. Seven facies are recognised from core and thin section characterisation and are grouped into three carbonate turbidite sequences. They include: 1) Calciturbidites, comprising mostly of highto low-density, wavy-laminated bioclast-rich facies; 2) low-density densite mudstones which are characterised by planar laminated and unlaminated muddominated facies; and 3) Calcidebrites which are muddy or hyper-concentrated debrisflow deposits occurring as poorly-sorted, chaotic, mud-supported floatstones. These

Phd by thesis

I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Principles of Neural Science

1. Introduction. Control System Design Steps. Adaptive Control. A Brief History. 2. Models for Dynamic Systems. Introduction. State-Space Models. Input/Output Models. Plant Parametric Models. Problems. 3. Stability. Introduction. Preliminaries. Input/Output Stability. Lyapunov Stability. Positive Real Functions and Stability. Stability of LTI Feedback System. Problems. 4. On-Line Parameter Estimation. Introduction. Simple Examples. Adaptive Laws with Normalization. Adaptive Laws with Projection. Bilinear Parametric Model. Hybrid Adaptive Laws. Summary of Adaptive Laws. Parameter Convergence Proofs. Problems. 5. Parameter Identifiers and Adaptive Observers. Introduction. Parameter Identifiers. Adaptive Observers. Adaptive Observer with Auxiliary Input. Adaptive Observers for Nonminimal Plant Models. Parameter Convergence Proofs. Problems. 6. Model Reference Adaptive Control. Introduction. Simple Direct MRAC Schemes. MRC for SISO Plants. Direct MRAC with Unnormalized Adaptive Laws. Direct MRAC with Normalized Adaptive Laws. Indirect MRAC. Relaxation of Assumptions in MRAC. Stability Proofs in MRAC Schemes. Problems. 7. Adaptive Pole Placement Control. Introduction. Simple APPC Schemes. PPC: Known Plant Parameters. Indirect APPC Schemes. Hybrid APPC Schemes. Stabilizability Issues and Modified APPC. Stability Proofs. Problems. 8. Robust Adaptive Laws. Introduction. Plant Uncertainties and Robust Control. Instability Phenomena in Adaptive Systems. Modifications for Robustness: Simple Examples. Robust Adaptive Laws. Summary of Robust Adaptive Laws. Problems. 9. Robust Adaptive Control Schemes. Introduction. Robust Identifiers and Adaptive Observers. Robust MRAC. Performance Improvement of MRAC. Robust APPC Schemes. Adaptive Control of LTV Plants. Adaptive Control for Multivariable Plants. Stability Proofs of Robust MRAC Schemes. Stability Proofs of Robust APPC Schemes. Problems. Appendices. Swapping Lemmas. Optimization Techniques. Bibliography. Index. License Agreement and Limited Warranty.

Robust adaptive control

A simplified procedure for the solution of the spread model proposed by Hill (1963) is presented for situations where the strip width is greater than the roll-gap length. The method utilizes a perturbation expansion for the width function and results in a simple analytical solution, overcoming the difficulties involved in solving the original complex, nonlinear, fourth-order differential equation. The solution provides insight into the relationship between the spread predicted and the roll gap parameters, and allows the assumptions made to be evaluated as leading order estimates to the stresses are produced.

Asymptotic Approach to the Hill Model of Spread in Rolling

This contribution considers the design of servo tracking loops for large antennae structures which exhibit lightly damped flexible modes. A cascaded loop control structure employing performance oriented adaptive feedforward in the outer loop is developed. The procedures for designing the inner current (motor drive), rate loop and position loop as well as the adaptive feedforward compensator are presented. It is demonstrated that this constitutes a practical and robust solution to the problem of controlling servo tracking systems in the presence of non trivial resonances. The control structure provides excellent tracking of parabolic trajectories as well as disturbance rejection. The conclusions are supported by analysis, simulation, and experimental results on 22m, 26m and 64m radio-astronomy and telecommunication antennae across Australia.

Adaptive Servo Control of Large Antenna Structures

Design limitations due to nonminimum phase plant zeros have been extensively studied for unity feedback systems. We now study these limitations in a general feedback architecture that allows for arbitrary exogenous and control inputs and performance and measured outputs. Motivation to do so is that many feedback systems have more complex architectures than the single input, single output architecture typically used to study design limitations. While it is difficult to make concrete statements without assuming additional structure, it is also possible to demonstrate that certain design limitations are present in very general circumstances. The results are applied to the problems of command tracking and disturbance attenuation for a military tank.

Nonminimum phase zeros in the general feedback configuration

In this paper, a consensus problem is studied for a group of second-order nonlinear heterogeneous agents with uniform time delay in communication links and uncertainty in agent dynamics. We design a class of novel decentralized control protocols for the consensus problem whose solvability is converted into stability analysis of an associated closed-loop system with uncertainty and time delay. Using an explicitly constructed Lyapunov functional, stability conditions and solvability conditions of the consensus problem are given in terms of a set of linear matrix inequalities (LMIs) apart from a small number of scalar parameters that appear nonlinearly. The effectiveness of the proposed control protocol is illustrated by numerical examples.

Consensus of a class of second-order nonlinear heterogeneous multi-agent systems with uniform time delay

Recently techniques have been developed for the calculation of PID controller gains based on frequency domain approximation using least squares methods. This paper presents methods for auto-tuning PID controllers based on limited frequency response information, using a weighted least squares approximation to a desired open loop transfer function. Automated techniques used for the calculation of this desired open loop transfer function are described, and simulation results of the auto-tuner are presented. It is shown that the auto-tuner performs well, and effectively designs PID controllers in situations where other auto-tuning techniques have not, including the case of mildly oscillatory systems.

Richard H. Middleton

Papers

Asymptotic Approach to the Hill Model of Spread in Rolling

Adaptive Servo Control of Large Antenna Structures

Nonminimum phase zeros in the general feedback configuration

Consensus of a class of second-order nonlinear heterogeneous multi-agent systems with uniform time delay

Auto-tuning PID controller design using frequency domain approximation