Preface |
Overview / 1: |
Methods of Operator Approximation in System Modelling / I: |
Nonlinear Operator Approximation with Preassigned Accuracy / 2: |
Introduction / 2.1: |
Generic formulation of the problem / 2.2: |
Operator approximation in space C([0; 1]) / 2.3: |
Operator approximation in Banach spaces by polynomial operators / 2.4: |
Approximation on compact sets in topological vector spaces / 2.5: |
Approximation on noncompact sets in Hilbert spaces / 2.6: |
Special results for maps into Banach spaces / 2.7: |
Concluding remarks / 2.8: |
Interpolation of Nonlinear Operators 65 / 3: |
Lagrange interpolation in Banach spaces / 3.1: |
Weak interpolation of nonlinear operators / 3.3: |
Some related results / 3.4: |
Realistic Operators and their Approximation / 3.5: |
Formalization of concepts related to description of real-world objects / 4.1: |
Approximation of RÂícontinuous operators / 4.3: |
Methods of Best Approximation for Nonlinear Operators / 4.4: |
Best Approximation of nonlinear operators in Banach spaces: Deterministic case / 5.1: |
Estimation of mean and covariance matrix for random vectors / 5.3: |
Best Hadamard-quadratic approximation / 5.4: |
Best polynomial approximation / 5.5: |
Best causal approximation / 5.6: |
Best hybrid approximations / 5.7: |
Optimal Estimation of Random Vectors / 5.8: |
Computational Methods for Optimal Filtering of Stochastic Signals / 6: |
Optimal linear Filtering in Finite dimensional vector spaces / 6.1: |
Optimal linear Filtering in Hilbert spaces / 6.3: |
Optimal causal linear Filtering with piecewise constant memory / 6.4: |
Optimal causal polynomial Filtering with arbitrarily variable memory / 6.5: |
Optimal nonlinear Filtering with no memory constraint / 6.6: |
Computational Methods for Optimal Compression and Reconstruction of Random Data / 6.7: |
Standard Principal Component Analysis and Karhunen-Loeeve transform (PCA{KLT) / 7.1: |
Rank-constrained matrix approximations / 7.3: |
Generic PCA{KLT / 7.4: |
Optimal hybrid transform based on Hadamard-quadratic approximation / 7.5: |
Optimal transform formed by a combination of nonlinear operators / 7.6: |
Optimal generalized hybrid transform / 7.7: |
Bibliography / 7.8: |
Index |
Preface |
Overview / 1: |
Methods of Operator Approximation in System Modelling / I: |
Nonlinear Operator Approximation with Preassigned Accuracy / 2: |
Introduction / 2.1: |
Generic formulation of the problem / 2.2: |