Communications and control for electric power systems: Power system stability applications of artificial neural networks

This report investigates the application of artificial neural networks to the problem of power system stability. The field of artificial intelligence, expert systems, and neural networks is reviewed. Power system operation is discussed with emphasis on stability considerations. Real-time system control has only recently been considered as applicable to stability, using conventional control methods. The report considers the use of artificial neural networks to improve the stability of the power system. The networks are considered as adjuncts and as replacements for existing controllers. The optimal kind of network to use as an adjunct to a generator exciter is discussed

Supervised Learning - An Introduction:Lectures given at the 30th Canary Islands Winter School of Astrophysics

Based on a set of lectures given at the 30th Canary Islands Winter School of Astrophysics: Big Data Analysis in Astronomy, La Laguna, Tenerife, Spain, 11/2018To a large extent, the material is taken from an MSc level course Neural Networks and Computational IntelligenceComputing Science Programme, University of GroningenThese notes present a selection of topics in the area of supervised machine learning. The focus is on the discussion of methods and algorithms for classification tasks. Regression by neural networks is discussedonly very briefly as it is in the center of complementary lectures. The same applies to concepts and methods of unsupervised learning.The selection and presentation of the material is clearly influenced by personal biasses and preferences. Nevertheless, the lectures and notes should provide a useful, albeit incomplete, overview and serve as astarting point for further exploration of the fascinating area of machine learning

The Shallow and the Deep:A biased introduction to neural networks and old school machine learning

The Shallow and the Deep is a collection of lecture notes that offers an accessible introduction to neural networks and machine learning in general. However, it was clear from the beginning that these notes would not be able to cover this rapidly changing and growing field in its entirety. The focus lies on classical machine learning techniques, with a bias towards classification and regression. Other learning paradigms and many recent developments in, for instance, Deep Learning are not addressed or only briefly touched upon.Biehl argues that having a solid knowledge of the foundations of the field is essential, especially for anyone who wants to explore the world of machine learning with an ambition that goes beyond the application of some software package to some data set. Therefore, The Shallow and the Deep places emphasis on fundamental concepts and theoretical background. This also involves delving into the history and pre-history of neural networks, where the foundations for most of the recent developments were laid. These notes aim to demystify machine learning and neural networks without losing the appreciation for their impressive power and versatility

Learning to See Analogies: A Connectionist Exploration

The goal of this dissertation is to integrate learning and analogy-making. Although learning and analogy-making both have long histories as active areas of research in cognitive science, not enough attention has been given to the ways in which they may interact. To that end, this project focuses on developing a computer program, called Analogator, that learns to make analogies by seeing examples of many different analogy problems and their solutions. That is, it learns to make analogies by analogy. This approach stands in contrast to most existing computational models of analogy in which particular analogical mechanisms are assumed a priori to exist. Rather than assuming certain principles about analogy-making mechanisms, the goal of the Analogator project is to learn what it means to make an analogy. This unique notion is the focus of this dissertation

Learning to See Analogies: A Connectionist Exploration

The goal of this dissertation is to integrate learning and analogy-making. Although learning and analogy-making both have long histories as active areas of research in cognitive science, not enough attention has been given to the ways in which they may interact. To that end, this project focuses on developing a computer program, called Analogator, that learns to make analogies by seeing examples of many different analogy problems and their solutions. That is, it learns to make analogies by analogy. This approach stands in contrast to most existing computational models of analogy in which particular analogical mechanisms are assumed a priori to exist. Rather than assuming certain principles about analogy-making mechanisms, the goal of the Analogator project is to learn what it means to make an analogy. This unique notion is the focus of this dissertation