THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

The Design and Analysis of Experiments

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

On the truck dispatching problem

The notion of fuzziness as defined in this paper relates to situations in which the source of imprecision is not a random variable or a stochastic process, but rather a class or classes which do not possess sharply defined boundaries, e.g., the “class of bald men,” or the “class of numbers which are much greater than 10,” or the “class of adaptive systems,” etc. A basic concept which makes it possible to treat fuzziness in a quantitative manner is that of a fuzzy set, that is, a class in which there may be grades of membership intermediate between full membership and non-membership. Thus, a fuzzy set is characterized by a membership function which assigns to each object its grade of membership (a number lying between 0 and 1) in the fuzzy set. After a review of some of the relevant properties of fuzzy sets, the notions of a fuzzy system and a fuzzy class of systems are introduced and briefly analyzed. The paper closes with a section dealing with optimization under fuzzy constraints in which an approach to...

Fuzzy sets and systems

Solar photovoltaic generation forecasting methods: A review

Adaptive neuro-fuzzy maximal power extraction of wind turbine with continuously variable transmission

Wind energy has become a large contender of traditional fossil fuel energy, particularly with the successful operation of multi-megawatt sized wind turbines. However, reasonable wind speed is not adequately sustainable everywhere to build an economical wind farm. In wind energy conversion systems, one of the operational problems is the changeability and fluctuation of wind. In most cases, wind speed can vacillate rapidly. Hence, quality of produced energy becomes an important problem in wind energy conversion plants. Several control techniques have been applied to improve the quality of power generated from wind turbines. In this study, the adaptive neuro-fuzzy inference system (ANFIS) is designed and adapted to estimate optimal power coefficient value of the wind turbines. Neural network in ANFIS adjusts parameters of membership function in the fuzzy logic of the fuzzy inference system (FIS). The back propagation learning algorithm is used for training this network. This intelligent controller is implemented using Matlab/Simulink and the performances are investigated. The simulation results presented in this paper show the effectiveness of the developed method.

Adaptive neuro-fuzzy approach for wind turbine power coefficient estimation

Support vector regression methodology for wind turbine reaction torque prediction with power-split hydrostatic continuous variable transmission

Wind farm efficiency by adaptive neuro-fuzzy strategy

Heart rate signal can be used as certain indicator of heart disease. Spectral analysis of heart rate variability (HRV) signal makes it possible to partly separate the low-frequency (LF) sympathetic component, from the high-frequency (HF) vagal component of autonomic cardiac control. Here, we used two important features to characterize the nonlinear fluctuations in the heart variability signal (HRV): cardiac vagal index (CVI) and cardiac sympathetic index (CSI) which indicates vagal and sympathetic function separately. This article presents a methodology for analyzing the influence of CVI and CSI on heart rate variability spectral patterns—low-frequency (LF) and high-frequency (HF) spectral bands and LF/HF ratio. An adaptive neuro-fuzzy network is used to approximate correlation between these two features and spectral patterns. This system is capable to find any change in ratio of features and spectral patterns of heart rate variability signal (HRV) and thus indicates state of both parasympathetic and sympathetic functions in newly diagnosed patients with heart diseases.

Žarko Ćojbašić

Papers

Adaptive neuro-fuzzy maximal power extraction of wind turbine with continuously variable transmission

Adaptive neuro-fuzzy approach for wind turbine power coefficient estimation

Support vector regression methodology for wind turbine reaction torque prediction with power-split hydrostatic continuous variable transmission

Wind farm efficiency by adaptive neuro-fuzzy strategy

Adaptive neuro-fuzzy estimation of autonomic nervous system parameters effect on heart rate variability