Saman Siadati

In artificial neural networks, the activation function of a node defines the output of that node given an input or set of inputs. A standard integrated circuit can be seen as a digital network of activation functions that can be "ON" (1) or "OFF" (0), depending on the input. This is similar to the behavior of the linear perceptron in neural networks. However, only nonlinear activation functions allow such networks to compute nontrivial problems using only a small number of nodes, and such activation functions are called nonlinearities.

Artificial neural networks (ANN) or connectionist systems are computing systems vaguely inspired by the biological neural networks that constitute animal brains. The data structures and functionality of neural nets are designed to simulate associative memory. Neural nets learn by processing examples, each of which contains a known "input" and "result," forming probability-weighted associations between the two, which are stored within the data structure of the net itself. (The "input" here is more accurately called an input set, since it generally consists of multiple independent variables, rather than a single value.) Thus, the "learning" of a neural net from a given example is the difference in the state of the net before and after processing the example. After being given a sufficient number of examples, the net becomes capable of predicting results from inputs, using the associations built from the example set. If a feedback loop is prov...