Application Testing of Novel Neural Network Structures Leon

The paper describes applications of a modified time-based multilayer perceptron (MTBMLP), which is a complex structure composed from a few time-based multilayer perceptrons with a reduced connectivity. The modification reduces connections, isolates information for each function and produces knowledge about the system of functions as a whole. This neural network is applied for change detection in signals delivered by sensor networks and for edge detection in image processing. In both applications a MTBMLP is utilized for function predictions and, after a further structure development is implemented, for an error prediction also. In sensor network applications, a number of experiments with Crossbow sensor kits and the MTBMLP acting as a function predictor have been conducted and analyzed for detecting a significant change in signals of various shapes and nature. A series of experiments with Lena image have been conducted for edge detection applications. The results demonstrate that MTBMLP is more efficient and reliable than other methodologies in sensor network change detection and that its application in in edge detection is also feasible.