Parameter Estimation of Sigmoid Superpositions: Dynamical System Approach
From MaRDI portal
Publication:4461330
DOI10.1162/089976603322362428zbMath1085.68653arXivmath/0207075OpenAlexW2137571295WikidataQ35544472 ScholiaQ35544472MaRDI QIDQ4461330
Danil Prokhorov, Cees van Leeuwen, I. Yu. Tyukin
Publication date: 30 March 2004
Published in: Neural Computation (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/math/0207075
Related Items
Cites Work
- Optimization by Simulated Annealing
- Parameter identification for uncertain plants using \(H^ \infty\) methods
- Absolute stability of global pattern formation and parallel memory storage by competitive neural networks
- Adaptive observers for single output nonlinear systems
- Stable adaptive observers for nonlinear time-varying systems
- A new adaptive learning rule
- Adaptive observers with exponential rate of convergence
- Universal approximation bounds for superpositions of a sigmoidal function
- Degree of Approximation Results for Feedforward Networks Approximating Unknown Mappings and Their Derivatives
- The unreasonable effectiveness of neural network approximation
- A Global Optimum Approach for One-Layer Neural Networks
- Parameter identification for uncertain linear systems with partial state measurements under an H/sup ∞/ criterion
- Adaptive observers with arbitrary exponential rate of convergence for nonlinear systems
- On continuous-time parameter identification by using observers
- Global adaptive output-feedback control of nonlinear systems. I. Linear parameterization
- Approximation by superpositions of a sigmoidal function