Markov chain network training and conservation law approximations: Linking microscopic and macroscopic models for evolution
From MaRDI portal
Publication:924419
DOI10.1016/j.amc.2007.09.063zbMath1137.68050OpenAlexW2019178565MaRDI QIDQ924419
Publication date: 16 May 2008
Published in: Applied Mathematics and Computation (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.amc.2007.09.063
neural networksMarkov chain approximation methodcomplex dynamic systemsconservation law approximationstraining dynamics
Problems related to evolution (92D15) Learning and adaptive systems in artificial intelligence (68T05)
Related Items (2)
The full positive flows of Manakov hierarchy, Hamiltonian structures and conservation laws ⋮ First passage time for multivariate jump-diffusion processes in finance and other areas of applications
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unsolved problems in geometry
- Dynamic system evolution and Markov chain approximation
- Multilayer feedforward networks are universal approximators
- Gödel's theorem and information
- Learning algorithm and retrieval process for the multiple classes random neural network model
- On approximate learning by multi-layered feedforward circuits
- ANN approach to sorption hysteresis within a coupled hygro-thermo-mechanical FE analysis
- Stabilizing and robustifying the learning mechanisms of artificial neural networks in control engineering applications
- Adaptive control of discrete-time systems using multiple models
- Universal approximation bounds for superpositions of a sigmoidal function
- On consistent regularities of control and value functions
- Neural-based direct adaptive control for a class of general nonlinear systems
- Issues in the application of neural networks for tracking based on inverse control
- Adaptive-critic-based neural networks for aircraft optimal control
- Non‐linear coupled CNN models for multiscale image analysis
- Approximation by superpositions of a sigmoidal function
This page was built for publication: Markov chain network training and conservation law approximations: Linking microscopic and macroscopic models for evolution
