✦ LIBER ✦

Characteristic Functions and Process Identification by Neural Networks

✍ Scribed by Joaquim A. Dente; Rui Vilela Mendes

Publisher: Elsevier Science
Year: 1997
Tongue: English
Weight: 498 KB
Volume: 10
Category: Article
ISSN: 0893-6080
DOI: 10.1016/s0893-6080(97)00040-3

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✦ Synopsis

Principal component analysis (PCA) algorithms use neural networks to extract the eigenvectors of the correlation matrix from the data. However, if the process is non-Gaussian, PCA algorithms or their higher order generalisations provide only incomplete or misleading information on the statistical properties of the data. To handle such situations we propose neural network algorithms, with an hybrid (supervised and unsupervised) learning scheme, which constructs the characteristic function of the probability distribution and the transition functions of the stochastic process. Illustrative examples are presented, which include Cauchy and Le ´vy-type processes. ᭧ 1997 Elsevier Science Ltd.

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