| | |
| | |
Stat |
Members: 3645 Articles: 2'503'724 Articles rated: 2609
23 April 2024 |
|
| | | |
|
Article overview
| |
|
Linking Microscopic and Macroscopic Models for Evolution: Markov Chain Network Training and Conservation Law Approximations | Roderick V.N. Melnik
; | Date: |
25 Feb 2007 | Subject: | Computational Engineering, Finance, and Science; Information
Theory; Numerical Analysis; Neural and Evolutionary Computing | Abstract: | In this paper, a general framework for the analysis of a connection between the training of artificial neural networks via the dynamics of Markov chains and the approximation of conservation law equations is proposed. This framework allows us to demonstrate an intrinsic link between microscopic and macroscopic models for evolution via the concept of perturbed generalized dynamic systems. The main result is exemplified with a number of illustrative examples where efficient numerical approximations follow directly from network-based computational models, viewed here as Markov chain approximations. Finally, stability and consistency conditions of such computational models are discussed. | Source: | arXiv, cs/0702148 | Services: | Forum | Review | PDF | Favorites |
|
|
No review found.
Did you like this article?
Note: answers to reviews or questions about the article must be posted in the forum section.
Authors are not allowed to review their own article. They can use the forum section.
browser Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Gecko; compatible; ClaudeBot/1.0; +claudebot@anthropic.com)
|
| |
|
|
|
| News, job offers and information for researchers and scientists:
| |