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

Considers a frequency domain, input-output based approach to the analysis of hybrid sampled-data systems. Based on previous works, the authors expound a frequency-domain lifting technique as an alternative to the time-domain lifting framework for sampled-data systems. In some cases, the approach pursued here can provide considerably simpler, more intuitive methods than time-domain lifting techniques to the analysis of the same problems. As applications, the authors consider the induced L/sub 2/ norms of the hybrid sensitivity and complementary sensitivity operators and the stability robustness of the hybrid system to linear time-invariant perturbations. In particularly for the single-input single-output case, "closed form" expressions arise for the induced norms, which allow straightforward numerical implementation.

http://ieeexplore.ieee.org/iel3/3920/11351/00520902.pdf

Sensitivity and robustness of sampled-data control systems: a frequency domain viewpoint

Many different types of adaptive or universal controllers", capable of dealing with a very broad range of linear time-invariant systems, have been proposed. One class of such controllers uses switching between a finite, or at least countable, number of fixed controllers until stability is detected. Such controllers are very attractive from a theoretical viewpoint, in providing stabilization and asymptotic performance for a broad class of plants. However, such controllers are also known to have very poor transient properties, due to the long time required to search for a stabilizing feedback. The key contribution of this paper is to introduce the "method of localization" which can greatly improve the speed of the search. The method is described for linear time-invariant discrete time systems of known nominal order, with disturbances and noise present. Analysis and simulations demonstrate the potential for greatly improved transient performance.

Localization based switching adaptive controllers

The transition operator that describes the time evolution of the state probability distribution for continuous-state linear systems is given by an integral operator. A state-discretization approach is proposed, which consists of a finite rank approximation of this integral operator. As a result of the state-discretization procedure, a Markov chain is obtained, in which case the transition operator is represented by a transition matrix. Spectral properties of the integral operator for the continuous-state case are presented. The relationships between the integral operator and the finite rank approximation are explored. In particular, the limiting properties of the eigenvalues of the transition matrices of the resulting Markov chains are studied in connection to the eigenvalues of the original continuous-state integral operator.

/pdf/convergence-of-eigenvalues-in-state-discretization-of-linear-298hmj1x6y.pdf

Convergence of Eigenvalues in State-Discretization of Linear Stochastic Systems

This paper proposes the use of a narrowband high frequency based distributed transformer model for estimating partial discharge location within the winding of an interleaved power transformer. At high frequencies, the residual inductance within a transformer winding becomes significant This inductive interaction generates its own characteristic response which, due to its distributed nature, can be utilized for partial discharge location. The technique proposed here takes advantage of the high frequency nature of a partial discharge and requires measurement at one location only. It also does not require detailed knowledge of the transformer's physical and material specifications. To confirm this approach, practical tests were conducted on a 66 kV/25 MVA interleaved transformer winding. The partial discharge injection was delivered by an oil immersed point-plane 7.5 kV source with the data captured using a current probe on the earth terminated neutral.

/pdf/practical-implementation-of-a-narrowband-high-frequency-18z930567w.pdf

Practical implementation of a narrowband high frequency distributed model for locating partial discharge in a power transformer

This chapter presents a new systematic switching control approach to adaptive stabilization of uncertain discrete-time systems. The approach is based on a method of localization that is conceptually different from supervisory adaptive control schemes and other existing switching control schemes. The proposed approach allows for slow parameter drifting, infrequent large parameter jumps, and unknown bound on exogenous disturbances. The unique feature of the localization-based switching adaptive control, proposed in the chapter, is its rapid model falsification capability. In the LTI case this is manifested in the ability of the switching controller to quickly converge to a suitable stabilizing controller. It is believed that the approach is applicable to a wide class of linear time invariant and time-varying systems with good transient performance.

Richard H. Middleton

Papers

Sensitivity and robustness of sampled-data control systems: a frequency domain viewpoint

Localization based switching adaptive controllers

Convergence of Eigenvalues in State-Discretization of Linear Stochastic Systems

Practical implementation of a narrowband high frequency distributed model for locating partial discharge in a power transformer

Adaptive stabilization of uncertain discrete-time systems via switching control: The method of localization