Characteristic functions and process identification by neural networks

Details Characteristic functions and process identification by neural networks Characteristic functions and process identification by neural networks

1997-12-17

arxiv.org/abs/physics/9712035v1

Neural Networks,10 (1997) 1465-1471

Physics

Data Analysis, Statistics and Probability

11 pages Latex, 12 figures in a combined ps-file

Scientific paper

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 Levy-type processes

Affiliated with

Also associated with

No associations

Rating

Characteristic functions and process identification by neural networks does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Characteristic functions and process identification by neural networks, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Characteristic functions and process identification by neural networks will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-677736