Robust artificial neural networks and outlier detection. Technical report

Large outliers break down linear and nonlinear regression models. Robust regression methods allow one to filter out the outliers when building a model. By replacing the traditional least squares criterion with the least trimmed squares criterion, in which half of data is treated as potential outliers, one can fit accurate regression models to strongly contaminated data. High-breakdown methods have become very well established in linear regression, but have started being applied for non-linear regression only recently. In this work, we examine the problem of fitting artificial neural networks to contaminated data using least trimmed squares criterion. We introduce a penalized least trimmed squares criterion which prevents unnecessary removal of valid data. Training of ANNs leads to a challenging non-smooth global optimization problem. We compare the efficiency of several derivative-free optimization methods in solving it, and show that our approach identifies the outliers correctly when ANNs are used for nonlinear regression

Modeling of Nonlinear Aggregation for Information Fusion Systems with Outliers Based on the Choquet Integral

Modern information fusion systems essentially associate decision-making processes with multi-sensor systems. Precise decision-making processes depend upon aggregating useful information extracted from large numbers of messages or large datasets; meanwhile, the distributed multi-sensor systems which employ several geographically separated local sensors are required to provide sufficient messages or data with similar and/or dissimilar characteristics. These kinds of information fusion techniques have been widely investigated and used for implementing several information retrieval systems. However, the results obtained from the information fusion systems vary in different situations and performing intelligent aggregation and fusion of information from a distributed multi-source, multi-sensor network is essentially an optimization problem. A flexible and versatile framework which is able to solve complex global optimization problems is a valuable alternative to traditional information fusion. Furthermore, because of the highly dynamic and volatile nature of the information flow, a swift soft computing technique is imperative to satisfy the demands and challenges. In this paper, a nonlinear aggregation based on the Choquet integral (NACI) model is considered for information fusion systems that include outliers under inherent interaction among feature attributes. The estimation of interaction coefficients for the proposed model is also performed via a modified algorithm based on particle swarm optimization with quantum-behavior (QPSO) and the high breakdown value estimator, least trimmed squares (LTS). From simulation results, the proposed MQPSO algorithm with LTS (named LTS-MQPSO) readily corrects the deviations caused by outliers and swiftly achieves convergence in estimating the parameters of the proposed NACI model for the information fusion systems with outliers

Machine learning methods for the estimation of weather and animal-related power outages on overhead distribution feeders

Doctor of PhilosophyDepartment of Electrical and Computer EngineeringSanjoy Das and Anil PahwaBecause a majority of day-to-day activities rely on electricity, it plays an important role in daily life. In this digital world, most of the people’s life depends on electricity. Without electricity, the flip of a switch would no longer produce instant light, television or refrigerators would be nonexistent, and hundreds of conveniences often taken for granted would be impossible. Electricity has become a basic necessity, and so any interruption in service due to disturbances in power lines causes a great inconvenience to customers. Customers and utility commissions expect a high level of reliability. Power distribution systems are geographically dispersed and exposure to environment makes them highly vulnerable part of power systems with respect to failures and interruption of service to customers. Following the restructuring and increased competition in the electric utility industry, distribution system reliability has acquired larger significance. Better understanding of causes and consequences of distribution interruptions is helpful in maintaining distribution systems, designing reliable systems, installing protection devices, and environmental issues. Various events, such as equipment failure, animal activity, tree fall, wind, and lightning, can negatively affect power distribution systems. Weather is one of the primary causes affecting distribution system reliability. Unfortunately, as weather-related outages are highly random, predicting their occurrence is an arduous task. To study the impact of weather on overhead distribution system several models, such as linear and exponential regression models, neural network model, and ensemble methods are presented in this dissertation. The models were extended to study the impact of animal activity on outages in overhead distribution system. Outage, lightning, and weather data for four different cities in Kansas of various sizes from 2005 to 2011 were provided by Westar Energy, Topeka, and state climate office at Kansas State University weather services. Models developed are applied to estimate daily outages. Performance tests shows that regression and neural network models are able to estimate outages well but failed to estimate well in lower and upper range of observed values. The introduction of committee machines inspired by the ‘divide & conquer” principle overcomes this problem. Simulation results shows that mixture of experts model is more effective followed by AdaBoost model in estimating daily outages. Similar results on performance of these models were found for animal-caused outages