ANN MODELS
Neural network models in artificial intelligence are essentially simple mathematical models defining
a functionf:X->Y or a distribution over X or both X and Y, but sometimes models are also intimately associated
with a particular learning algorithm or learning rule.
A common use of the ANN model really means the definition of a class of such functions
(where members of the class are obtained by varying parameters, connection weights, or
specifics of the architecture such as the number of neurons or their connectivity).
Network function
The word network in the term 'artificial neural network' refers to the interconnections
between the neurons inthe different layers of each system.
An example system has three layers. The first layer has input neurons which send data via synapses
to the second layer of neurons, and then via more synapses to the third layer of output neurons.
More complex systems will have more layers of neurons with some having increased layers of input neurons
and output neurons. The synapses store parameters called "weights" that manipulate the data in the calculations.
An ANN is typically defined by three types of parameters:
-
The interconnection pattern between the differentlayers of neurons
-
The learning process for updating the weights of the interconnections
-
The activation function that converts a neuron's weighted input to its output activation.
