MULTILAYER PERCEPTRONS
Multilayer feedforward networks are one of the most important and most popular classes of ANNs in real-world applications. Typically, the network consists of a set of inputs that constitute the input layer of the network, one or more hidden layers of computational nodes, and finally an output layer of computational nodes. The processing is in a forward direction on a layer-by-layer basis. This type of artificial neural networks are commonly referred to as multilayer perceptrons (MLPs), which represent a generalization of the simple perceptron, a network with a single layer, considered earlier in this chapter.
A multiplayer perceptron has three distinctive characteristics:
1. The model of each neuron in the network includes usually a nonlinear activation function, sigmoidal or hyperbolic.
2. The network contains one or more layers of hidden neurons that are not a part of the input or output of the network. These hidden nodes enable the network to learn complex and highly nonlinear tasks by extracting progressively more meaningful features from the input patterns.
3. The network exhibits a high degree of connectivity from one layer to the next one.