Artificial neural network/Neuron
Artificial neurons[edit | edit source]
ANNs are composed of artificial neurons which are conceptually derived from biological neurons. Each artificial neuron has inputs and produces a single output which can be sent to multiple other neurons. The inputs can be the feature values of a sample of external data, such as images or documents, or they can be the outputs of other neurons. The outputs of the final output neurons of the neural net accomplish the task, such as recognizing an object in an image.
To find the output of the neuron we take the weighted sum of all the inputs, weighted by the weights of the connections from the inputs to the neuron. We add a bias term to this sum. This weighted sum is sometimes called the activation. This weighted sum is then passed through a (usually nonlinear) activation function to produce the output. The initial inputs are external data, such as images and documents. The ultimate outputs accomplish the task, such as recognizing an object in an image.
See also[edit | edit source]
References[edit | edit source]
- Abbod, Maysam F (2007). "Application of Artificial Intelligence to the Management of Urological Cancer". The Journal of Urology 178 (4): 1150–1156. doi:10.1016/j.juro.2007.05.122. PMID 17698099.
- DAWSON, CHRISTIAN W (1998). "An artificial neural network approach to rainfall-runoff modelling". Hydrological Sciences Journal 43 (1): 47–66. doi:10.1080/02626669809492102.
- "The Machine Learning Dictionary". www.cse.unsw.edu.au. Archived from the original on 26 August 2018. Retrieved 4 November 2009.