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Nonlinear prediction and the Wiener process
K. C. Nisbet
, S. McLaughlin
, B. Mulgrew
Research output
:
Contribution to conference
›
Paper
4
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Citations (Scopus)
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Dive into the research topics of 'Nonlinear prediction and the Wiener process'. Together they form a unique fingerprint.
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Keyphrases
Probability Density Function
100%
Nonlinear Prediction
100%
Orthonormal Polynomials
100%
Wiener Process
100%
Wiener
75%
Radial Basis Function Neural Network (RBFNN)
75%
Orthonormal Functions
50%
Pseudo-inverse
50%
Eigenvalue Analysis
50%
Correlation Matrix
50%
Radial Basis Function
25%
Eigenvector
25%
Eigenvalues
25%
Least Squares
25%
Scalar
25%
Embedding Vector
25%
Matrix-based
25%
Volterra Type
25%
Theoretical Problems
25%
Volterra Expansion
25%
Power Series
25%
Volterra Analysis
25%
Orthonormal
25%
Three-stage Strategy
25%
Linear Combiner
25%
Higher-order Moments
25%
Mathematics
Probability Density Function
100%
Basis Function
100%
Nonlinear Prediction
100%
Polynomial
100%
Wiener Process
100%
Eigenvalue
75%
Correlation Matrix
50%
Orthonormal Function
50%
Pseudoinverse
50%
Matrix (Mathematics)
25%
Least Square
25%
Power Series
25%
Scalar Time
25%
Eigenvector
25%
Engineering
Probability Density Function
100%
Radial Basis Function Network
75%
Correlation Matrix
50%
Eigenvalue Analysis
50%
Radial Basis Function
25%
Eigenvector
25%
Eigenvalue
25%
Least Square
25%
Simulation Result
25%
Combiner
25%