Learn More
We present a general and systematic method for neural network design based on the genetic algorithm. The technique works in conjunction with network learning rules, addressing aspects of the network's gross architecture, connectivity, and learning rule parameters. Networks can be optimiled for various applicationspecific criteria, such as learning speed,(More)
A limiting factor for the application of IDA methods in many domains is the incompleteness of data repositories. Many records have fields that are not filled in, especially, when data entry is manual. In addition, a significant fraction of the entries can be erroneous and there may be no alternative but to discard these records. But every cell in a database(More)
Smart grids have become a topic of intensive research, development, and deployment across the world over the last few years. The engagement of consumer sectors—residential, commercial, and industrial—is widely acknowledged as a key requirement for the projected benefits of smart grids to be realized. Although the industrial sector has traditionally been(More)
A serious problem in mining industrial data bases is that they are often incomplete, and a significant amount of data is missing, or erroneously entered. This paper explores the use of machine-learning based alternatives to standard statistical data completion (data imputation) methods, for dealing with missing data. We have approached the data completion(More)
New developments in software and information technology are reinvigorating the control engineering community, raising expectations of dramatic improvements in the performance, safety, design time, and verification and validation of control systems. In concert with these developments, synergies between computer science and control are enabling futuristic(More)