Overview of Artificial Neural Networks

Over the past years, several technologies have been introduced to help the engineering industry analyze and design large-scale complex systems. One of these techniques is artificial neural network technology. The primary advantages of this technology are in the area of learning algorithms, on-line adaptation of dynamic systems, quick parallel computation and intelligent interpolation of data. This programme is a module of the video tutorial "Artificial Neural Networks with Applications to Power Systems". The purpose of this CD-ROM course is to provide an introduction to artificial neural network (ANN) technology for power system engineers. This tutorial gives an overview of ANNs, including network architectures, principles of operation, learning rules, advantages and limitations. The objective is to give the reader a working knowledge of ANN including examples. From this CD-ROM, the viewer will learn about neural network architecture and learning algorithms. The introduction, "Historical Perspectives on Neural-Net Computing", focuses on neural-net computing as a computational paradigm and relates it to research activities and achievements in several other disparate disciplines. "Introduction to Concepts in Artificial Neural Networks" summarizes some basic concepts of computational neuroscience and the resulting modules of artificial neurons, introducing biological and machine learning and neural processing terminology. The next section, "An Example of Unsupervised Networks Kohonen's Self-Organizing Feature Map", details supervised learning attempts to determine the relationship between a set of input or stimulus data and corresponding output data. "Neural Network and its Ancillary Techniques as Applied to Power Systems" addresses why neural networks are a useful problem solving tool in the power industry. The final section, "State-of-the-Art Overview on Artificial Neural Networks in Power Systems" describes ANNs in power systems, as well as how to apply the typical ANNs to specific problems.