The age of analog networks.: An article from: AI Magazine

This digital document is an article from AI Magazine, published by American Association for Artificial Intelligence on September 22, 2008. The length of the article is 7809 words. The page length shown above is based on a typical 300-word page. The article is delivered in HTML format and is available immediately after purchase. You can view it with any web browser.

From the author: A large class of systems of biological and technological relevance can be described as analog networks, that is, collections of dynamic devices interconnected by links of varying strength. Some examples of analog networks are genetic regulatory networks, metabolic networks, neural networks, analog electronic circuits, and control systems. Analog networks are typically complex systems that include nonlinear feedback loops and possess temporal dynamics at different time scales. Both the synthesis and reverse engineering of analog networks are recognized as knowledge-intensive activities, for which few systematic techniques exist. In this paper we will discuss the general relevance of the analog network concept and describe an evolutionary approach to the automatic synthesis and the reverse engineering of analog networks. The proposed approach is called analog genetic encoding (AGE) and realizes an implicit genetic encoding of analog networks. AGE permits the evolution of human-competitive solutions to real-world analog network design and identification problems. This is illustrated by some examples of application to the design of electronic circuits, control systems, learning neural architectures, and the reverse engineering of biological networks.

Citation Details
Title: The age of analog networks.
Author: Claudio Mattiussi
Publication:AI Magazine (Magazine/Journal)
Date: September 22, 2008
Publisher: American Association for Artificial Intelligence
Volume: 29 Issue: 3 Page: 63(14)

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