Artificial neural network/Models

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ANNs began as an attempt to exploit the architecture of the human brain to perform tasks that conventional algorithms had little success with. They soon reoriented towards improving empirical results, mostly abandoning attempts to remain true to their biological precursors. Neurons are connected to each other in various patterns, to allow the output of some neurons to become the input of others. The network forms a directed, weighted graph.[1]

An artificial neural network consists of a collection of simulated neurons. Each neuron is a node which is connected to other nodes via links that correspond to biological axon-synapse-dendrite connections. Each link has a weight, which determines the strength of one node's influence on another.[2]

See also[edit | edit source]

References[edit | edit source]

  1. Zell, Andreas (2003). "chapter 5.2". Simulation neuronaler Netze (in de) (1st ed.). Addison-Wesley. ISBN 978-3-89319-554-1. OCLC 249017987. 
  2. Artificial intelligence (3rd ed.). Addison-Wesley Pub. Co. 1992. ISBN 0-201-53377-4.