Optimization of approximating networks for optimal fault diagnosis
From MaRDI portal
Publication:5317748
DOI10.1080/10556780512331318245zbMath1072.90036OpenAlexW2077559968MaRDI QIDQ5317748
Angelo Alessandri, Marcello Sanguineti
Publication date: 21 September 2005
Published in: Optimization Methods and Software (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/10556780512331318245
stochastic approximationoptimal estimationfunctional optimizationmodel-based fault diagnosisnonlinear programinghigh-dimensional admissible solutionspolynomially complex approximators
Related Items (4)
Can dictionary-based computational models outperform the best linear ones? ⋮ New insights into Witsenhausen's counterexample ⋮ Management of water resource systems in the presence of uncertainties by nonlinear approximation techniques and deterministic sampling ⋮ Approximation schemes for functional optimization problems
Cites Work
- Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy - a survey and some new results
- Early warning of slight changes in systems
- Identification of time-varying systems with abrupt parameter changes
- On global univalence theorems
- Nonlinear black-box modeling in system identification: A unified overview
- Applying Experimental Design and Regression Splines to High-Dimensional Continuous-State Stochastic Dynamic Programming
- Robust fault isolation for a class of non-linear input?output systems
- A geometric approach to the synthesis of failure detection filters
- Numerical Solution of Continuous-State Dynamic Programs Using Linear and Spline Interpolation
- Universal approximation bounds for superpositions of a sigmoidal function
- Hinging hyperplanes for regression, classification, and function approximation
- Neural approximators for nonlinear finite-memory state estimation
- A geometric approach to nonlinear fault detection and isolation
- Numerical solutions to the Witsenhausen counterexample by approximating networks
- Bounds on rates of variable-basis and neural-network approximation
- Comparison of worst case errors in linear and neural network approximation
- Minimization of Error Functionals over Variable-Basis Functions
- Neural approximations for multistage optimal control of nonlinear stochastic systems
- Successive Galerkin approximation algorithms for nonlinear optimal and robust control
- A robust detection and isolation scheme for abrupt and incipient faults in nonlinear systems
- Approximating networks and extended Ritz method for the solution of functional optimization problems
This page was built for publication: Optimization of approximating networks for optimal fault diagnosis