Artificial neural networks represent such a biologically inspired paradigm. At the present time, neural networks represent an important paradigm for classifying patterns, or generating signals to control their environment. After training, the neural network can be used to predict a new output patterns, based on the input pattern only. The adaptation law that allows to adjust the connection weights is called the learning algorithm. This is of course reminiscent of the adaptive control strategy.
Neural networks can approximate nonlinear functions.
Once the connection weights have been trained, the neural network approximates the input-output mapping provided by the training set. Mathematically speaking, it means that a multi-layer neural network can approximate any continuous nonlinear function, provided that there is a sufficient number of hidden units in the hidden layers. Neural networks can be trained in noise. For instance, a neural network trained with a least mean square criterion approximates the conditional expectation of the output pattern, given the input pattern. There exists robust learning algorithms that guarantee convergence in the presence of uncertainties.