06/00938 The dynamic thermal characteristics of lightweight building elements with a forced ventilated cavity and radiation barriers: Cerne, B. and Medved, S. Energy and Buildings, 2005, 37, (9), 972–981.

Abstract

A new procedure for classifying bioprocess performance using artificial neural networks is presented. Using historical records of processes with known good and bad outcomes as reference sets, patterns in the data are represented as data clusters generated from the bottleneck values of an autoassociative neural network (AANN). The strategy is similar to analyzing the cluster of points generated when plotting the first and second principal components. This method departs, however, from principal component analysis (PCA) in that it can handle data clouds generated from more than 2 variables and the classification is assessed quantitatively as opposed to visually. This is achieved by feeding the cloud patterns into a feed-forward network which compares the sample’s data clouds to reference sets. Using a classification scheme based on Soft Independent Modeling of Class Analogy (SIMCA), the net is able to classify a process as good, bad, neither, or both as early as possible.