Permalink for this collection
Browse
Recent Submissions
Publication Open Access Further sensitivity analysis of simple evolving connectionist systems applied to the Lincoln aphid data set(Bio-Protection & Ecology Division, 2007) Watts, Michael J.; Worner, Susan P.This report presents two further experiments over the Aphid data set. The first is a more detailed investigation of the sensitivity of Simple Evolving Connectionist System (SECoS) networks to the exclusion of various combinations of inputs. This is in contrast to the previous work, where only the effect of excluding single variables was investigated. The second experiment investigates a hypothesis that attempts to explain the results found in the first experiment.Publication Open Access Comparison of multi-layer perceptrons and simple evolving connectionist systems over the Lincoln aphid data set(Bio-Protection & Ecology Division, 2007) Watts, Michael J.; Worner, Susan P.This report presents two further experiments over the aphid data set. The first is an evaluation of the adaptive abilities of backpropagation of errors trained MLP and a comparison of these capabilities with the Simple Evolving Connectionist System (SECoS). The goal of the first experiment is to compare both the performance and the adaptive abilities of the two models. The second experiment is an investigation of the sensitivity of the SECoS to the exclusion of various input variables. The goal of the second experiment is to determine which of the thirteen input variables contributes the most to the modelling of the problem, that is, which variable the network is most sensitive to.Publication Open Access Using multi-layer perceptrons to model the Lincoln aphid data set(Bio-Protection & Ecology Division, 2007) Watts, Michael J.; Worner, Susan P.This document is the initial report on a systematic approach to the application of MLP to the aphid prediction problem. The aims of this initial work are three-fold; to investigate the effectiveness of a particular representation of the data, to identify the approximate optimal topology for MLP applied to this problem, and to identify the approximate optimal training parameters for MLP applied to this problem.