One-unit rigid-bodies learning rule for principal/independent component analysis with application to ECT-NDE signal processing
The aim of this research work is to present a detailed theoretical analysis of the one-unit learning rule based on the rigid-bodies learning theory, specialized for first principal/independent component analysis. The adaptation equations are regarded as generators of weight-flows on a structured parameters space; the stationary points of the learning equations are determined and their stability is proven through a suitable Lyapunov function. The neuron is also excited with both synthetic and real-world signals in order to numerically investigate its behavior, and eddy-current-signal processing is carried out as an application of the developed independent component analysis algorithm to non-destructive evaluation of metallic objects.
independent component analysis one-unit neural system unsupervised learning theory principal component analysis rigid-body dynamics pattern recognition eddy-current (ec) phenomenon non-destructive evaluation (nde)