https://www2.econ.iastate.edu/tesfatsi/DeepLearningInNeuralNetworksOverview.JSchmidhuber2015.pdf

Deep learning in neural networks

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

[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

The purpose of this paper is to provide a comprehensive presentation and interpretation of the Ensemble Kalman Filter (EnKF) and its numerical implementation. The EnKF has a large user group, and numerous publications have discussed applications and theoretical aspects of it. This paper reviews the important results from these studies and also presents new ideas and alternative interpretations which further explain the success of the EnKF. In addition to providing the theoretical framework needed for using the EnKF, there is also a focus on the algorithmic formulation and optimal numerical implementation. A program listing is given for some of the key subroutines. The paper also touches upon specific issues such as the use of nonlinear measurements, in situ profiles of temperature and salinity, and data which are available with high frequency in time. An ensemble based optimal interpolation (EnOI) scheme is presented as a cost-effective approach which may serve as an alternative to the EnKF in some applications. A fairly extensive discussion is devoted to the use of time correlated model errors and the estimation of model bias.

The Ensemble Kalman Filter: Theoretical formulation and practical implementation

Reinforcement learning offers to robotics a framework and set of tools for the design of sophisticated and hard-to-engineer behaviors. Conversely, the challenges of robotic problems provide both inspiration, impact, and validation for developments in reinforcement learning. The relationship between disciplines has sufficient promise to be likened to that between physics and mathematics. In this article, we attempt to strengthen the links between the two research communities by providing a survey of work in reinforcement learning for behavior generation in robots. We highlight both key challenges in robot reinforcement learning as well as notable successes. We discuss how contributions tamed the complexity of the domain and study the role of algorithms, representations, and prior knowledge in achieving these successes. As a result, a particular focus of our paper lies on the choice between model-based and model-free as well as between value-function-based and policy-search methods. By analyzing a simple problem in some detail we demonstrate how reinforcement learning approaches may be profitably applied, and we note throughout open questions and the tremendous potential for future research.

/pdf/reinforcement-learning-in-robotics-a-survey-4w4397qx9i.pdf

Reinforcement learning in robotics: A survey

Most processes in industry are complex mul-tivariable systems with nonlinear and time-varying dynamics. These processes are often diicult to model. However, often much expert knowledge and process data is available. Fuzzy modeling and control techniques can eeciently integrate this information. However, most of the systems considered in literature are of low-order and SISO or MISO systems. For MIMO systems, complications occur because the size of the rule bases and computational load increases dramatically for higher order systems. Within the FAMIMO ESPRIT project see acknowledgements , new algorithms are being developed for the indentiication and control of MIMO systems. The main research items are: MIMO model identiication Fuzzy heterogeneous control Pseudo inversion of fuzzy models Fuzzy adaptive control Model based predictive control Methods for stability analysis Development of software tools New developments from our group and the partners in FAMIMO will be presented. In Delft, a software toolbox for MIMO Takagi-Sugeno 44 fuzzy model identiication is developed 11. Two methods for MBPC using these models are investigated. Namely, local linearized control 3 and a Branch & Bound algorithm. Both methods are incorporated in an internal model control IMC 2 scheme. Two benchmarks are used to compare the developed methods : Wastewater treatment of a paper factory Direct injection engine Acknowledgements: This work has been done

Fuzzy Control of Multivariable Processes

http://www.nt.ntnu.no/users/skoge/prost/proceedings/ifac11-proceedings/data/html/papers/0598.pdf

Efficient Knowledge Transfer in Shaping Reinforcement Learning

Systems and Control Engineering

A large class of nonlinear systems can be well approximated by Takagi-Sugeno fuzzy models, for which methods and algorithms have been developed to analyze their stability and to design observers and controllers. However, results obtained for Takagi-Sugeno fuzzy models are in general not directly applicable to the original nonlinear system. In this paper, we investigate what conclusions can be drawn when an observer-based controller is designed for an approximate model and then applied to the original nonlinear system. In particular, we consider the case when the scheduling vector used in the membership functions of the observer depends on the states that have to be estimated. The results are illustrated using simulation examples.

http://lendek.net/files/papers/bounds_appr_part2.pdf

Stability bounds for fuzzy estimation and control — Part II: Output-feedback control

http://www.dcsc.tudelft.nl/~bdeschutter/pub/rep/11_012.pdf

Robert Babuska

Papers

Fuzzy Control of Multivariable Processes

Efficient Knowledge Transfer in Shaping Reinforcement Learning

Systems and Control Engineering

Stability bounds for fuzzy estimation and control — Part II: Output-feedback control

Convergence Analysis of Ant Colony Learning