Describe different types of adaptive learning mechanisms that can be used for both adaptive fis and rbfn.
Answers
A Radial Basis Function Network (RBFN) is a particular type of neural network. In this article, I’ll be describing it’s use as a non-linear classifier.
Generally, when people talk about neural networks or “Artificial Neural Networks” they are referring to the Multilayer Perceptron (MLP). Each neuron in an MLP takes the weighted sum of its input values. That is, each input value is multiplied by a coefficient, and the results are all summed together. A single MLP neuron is a simple linear classifier, but complex non-linear classifiers can be built by combining these neurons into a network.
To me, the RBFN approach is more intuitive than the MLP. An RBFN performs classification by measuring the input’s similarity to examples from the training set. Each RBFN neuron stores a “prototype”, which is just one of the examples from the training set. When we want to classify a new input, each neuron computes the Euclidean distance between the input and its prototype. Roughly speaking, if the input more closely resembles the class A prototypes than the class B prototypes, it is classified as class A.
RBF Network Architecture
Architecture_Simple
The above illustration shows the typical architecture of an RBF Network. It consists of an input vector, a layer of RBF neurons, and an output layer with one node per category or class of data.