3 research outputs found
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System identification algorithms for the analysis of dielectric responses from broadband spectroscopies
We discuss the modeling of dielectric responses for an electromagnetically excited network of capacitors and resistors using a systems identification framework. Standard models that assume integral order dynamics are augmented to incorporate fractional order dynamics. This enables us to relate more faithfully the modeled responses to those reported in the Dielectrics literature
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High signal to noise ratio THz spectroscopy with ASOPS and signal processing schemes for mapping and controlling molecular and bulk relaxation processes
Asynchronous Optical Sampling has the potential to improve signal to noise ratio in
THz transient sperctrometry. The design of an inexpensive control scheme for synchronising
two femtosecond pulse frequency comb generators at an offset frequency of 20 kHz is
discussed. The suitability of a range of signal processing schemes adopted from the Systems
Identification and Control Theory community for further processing recorded THz transients in
the time and frequency domain are outlined. Finally, possibilities for femtosecond pulse
shaping using genetic algorithms are mentioned
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Neural and wavelet network models for financial distress classification
This work analyzes the use of linear discriminant models, multi-layer perceptron neural networks and wavelet networks for corporate financial distress prediction. Although simple and easy to interpret, linear models require statistical assumptions that may be unrealistic. Neural networks are able to discriminate patterns that are not linearly separable, but the large number of parameters involved in a neural model often causes generalization problems. Wavelet networks are classification models that implement nonlinear discriminant surfaces as the superposition of dilated and translated versions of a single "mother wavelet" function. In this paper, an algorithm is proposed to select dilation and translation parameters that yield a wavelet network classifier with good parsimony characteristics. The models are compared in a case study involving failed and continuing British firms in the period 1997-2000. Problems associated with over-parameterized neural networks are illustrated and the Optimal Brain Damage pruning technique is employed to obtain a parsimonious neural model. The results, supported by a re-sampling study, show that both neural and wavelet networks may be a valid alternative to classical linear discriminant models