1. Introduction and Overview. 2. Detecting Influential Observations and Outliers. 3. Detecting and Assessing Collinearity. 4. Applications and Remedies. 5. Research Issues and Directions for Extensions. Bibliography. Author Index. Subject Index.

Regression Diagnostics: Identifying Influential Data and Sources of Collinearity

On robust estimation of the location parameter

Information Theory and Reliable Communication

Classification is one of the most active research and application areas of neural networks. The literature is vast and growing. This paper summarizes some of the most important developments in neural network classification research. Specifically, the issues of posterior probability estimation, the link between neural and conventional classifiers, learning and generalization tradeoff in classification, the feature variable selection, as well as the effect of misclassification costs are examined. Our purpose is to provide a synthesis of the published research in this area and stimulate further research interests and efforts in the identified topics.

/pdf/neural-networks-for-classification-a-survey-573tgz3hdm.pdf

Neural networks for classification: a survey

https://uwspace.uwaterloo.ca/bitstream/handle/10012/3000/thesis.pdf;sequence=1

Cost-sensitive boosting for classification of imbalanced data

From the Publisher:
Fuzzy and Neural Approaches in Engineering presents a detailed examination of the fundamentals of fuzzy systems and neural networks and then joins them synergistically - combining the feature extraction and modeling capabilities of the neural network with the representation capabilities of fuzzy systems. Exploring the value of relating genetic algorithms and expert systems to fuzzy and neural technologies, this forward-thinking text highlights an entire range of dynamic possibilities within soft computing. With examples of specifically designed to illuminate key concepts and overcome the obstacles of notation and overly mathematical presentations often encountered in other sources, plus tables, figures, and an up-to-date bibliography, this unique work is both an important reference and a practical guide to neural networks and fuzzy systems.

Fuzzy and neural approaches in engineering

Vibration monitoring of components in manufacturing plants involves the collection of vibration data from plant components and detailed analysis to detect features that reflect the operational state of the machinery. The analysis leads to the identification of potential failures and their causes and makes it possible to perform efficient preventive maintenance. Work on the design of a vibration monitoring methodology for rolling element bearings (REB) based on neural network technology is presented. This technology provides an attractive complement to traditional vibration analysis because of the potential of neural networks to operate in real-time mode and to handle data that may be distorted or noisy. The significance of this work relies on the fact that REB failures are responsible for a large fraction of the malfunctions in manufacturing equipment. The technique enhances traditional vibration analysis and provides a means of automating the monitoring and diagnosis of vibrating equipment. >

Monitoring and diagnosis of rolling element bearings using artificial neural networks

In this work, the nuclear power plant operating status recognition issue is investigated using artificial neural networks (ANNs). The objective is to train an ANN to classify nuclear power plant accident conditions and to assess the potential of future work in the area of plant diagnostics with ANNS. To this end, an ANN was trained to recognize normal operating conditions as well as potentially unsafe conditions based on nuclear power plant training simulator generated accident scenarios. These scenarios include; hot and cold leg loss of coolant, control rod ejection, loss of offsite power, main steam line break, main feedwater line break and steam generator tube leak accidents. Findings show that ANNs can be used to diagnose and classify nuclear power plant conditions with good results.

Nuclear power plant status diagnostics using an artificial neural network

From the Publisher:
Neural networks and fuzzy systems represent two distinct technologies that deal with uncertainty. Researchers are applying neural networks and fuzzy systems in series, from the use of fuzzy inputs and outputs for neural networks to the employment of individual neural networks to quantify the shape of a fuzzy membership function. This book presents the fundamentals of both technologies, and combines the unique capabilities of these two technologies to gain the advantages of both.

MATLAB Supplement to Fuzzy and Neural Approaches in Engineering

Recently, the use of Autoassociative Neural Networks (AANNs) to perform on-line calibration monitoring of process sensors has been shown to not only be feasible, but practical as well. This paper summarizes the results of applying AANNs to instrument surveillance and calibration monitoring at Florida Power Corporation’s Crystal River #3 Nuclear Power Plant and at the Oak Ridge National Laboratory High Flux Isotope Reactor. In both cases sensor drifts are detectable at a nominal level of 0.5% instrument’s full scale range. This paper will discuss the selection of a five layer neural network architecture, a robust training paradigm, the input selection criteria, and a retuning algorithm.

Robert E. Uhrig

Papers

Fuzzy and neural approaches in engineering

Monitoring and diagnosis of rolling element bearings using artificial neural networks

Nuclear power plant status diagnostics using an artificial neural network

MATLAB Supplement to Fuzzy and Neural Approaches in Engineering

Use of Autoassociative Neural Networks for Signal Validation