ADALINE Architecture

ADALINE Architecture Description

This content is based in the [1] reference.

The general structure of the ADALINE type network is:
where:
p = input patterns
b = activation thresholds
a = neuron output

The output nets is given by:

 For an ADALNE network of a single neuron with two inputs the diagram corresponds to the following figure:

 In similarity to Perceptron, the limit of the decision characteristic for the ADALINE network is presented when n = 0, therefore:

 In the next figure, the line separating the input space in two regions, as shown in the following figure:

 The output of the neuron is major than zero in the gray area, in the white output area is less than zero. The Adaline network can correctly classify linearly separable patterns in two categories.

 The neuronal architecture has a layer of neurons connected to R inputs through a matrix of weights W.

This network is often called MADALINE or Multiple ADALINE. It defines an output vector a of length S.

The Widrow-Hoff rule can train only one layer of linear networks. This is not a disadvantage, since a single-layer network is as capable as a multi-layered network. For each multilayer linear network, there is a single-layer linear network.



References:

[1] Kim Seng Chia. Predicting the boiling point of diesel fuel using ADALINE and spectrum. University of Tun Hussein Malasia. 2015

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