Modular neural network: Difference between revisions

Revision as of 07:01, 7 August 2016

Revision as of 04:46, 9 July 2017

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{{Underlinked|date=December 2012}}

A '''modular neural network''' is an [[artificial neural network]] characterized by a series of independent neural networks moderated by some intermediary. Each independent neural network serves as a module and operates on separate inputs to accomplish some subtask of the task the network hopes to perform.<ref name="Azom, 2000">(Azam, 2000)</ref> The intermediary takes the outputs of each module and processes them to produce the output of the network as a whole. The intermediary only accepts the modules’modules' outputs—it does not respond to, nor otherwise signal, the modules. As well, the modules do not interact with each other.

==Biological basis==

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===Training===

A large [[neural network]] attempting to model multiple parameters can suffer from interference as new data can dramatically alter existing connections or just serve to confuse. With some foresight into the subtasks to be solved, each neural network can be tailored for its task. This means the training [[algorithm]] used, and the training data used for each sub-network can be unique and implemented much more quickly. In large part this is due to the possible combinations of interesting factors diminishing as the number of inputs decreases.

===Robustness===

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== Notes ==

{{reflist|1}}

== References ==