C++ Neural Networks and Fuzzy Logic: Preface

C++ Neural Networks and Fuzzy Logic

by Valluru B. Rao

MTBooks, IDG Books Worldwide, Inc.

ISBN: 1558515526   Pub Date: 06/01/95

Previous Table of Contents Next

Unsupervised Networks

Unsupervised neural network paradigms to be discussed include:

•  Hopfield Memory

•  Bidirectional associative memory

•  Fuzzy associative memory

•  Learning vector quantizer

•  Kohonen self−organizing map

•  ART1

Self−Organization

Unsupervised learning and self−organization are closely related. Unsupervised learning was mentioned in

Chapter 1, along with supervised learning. Training in supervised learning takes the form of external

exemplars being provided. The network has to compute the correct weights for the connections for neurons in

some layer or the other. Self−organization implies unsupervised learning. It was described as a characteristic

of a neural network model, ART1, based on adaptive resonance theory (to be covered in Chapter 10). With the

winner−take−all criterion, each neuron of field B learns a distinct classification. The winning neuron in a

layer, in this case the field B, is the one with the largest activation, and it is the only neuron in that layer that is

allowed to fire. Hence, the name winner take all.

Self−organization means self−adaptation of a neural network. Without target outputs, the closest possible

response to a given input signal is to be generated. Like inputs will cluster together. The connection weights

are modified through different iterations of network operation, and the network capable of self−organizing

creates on its own the closest possible set of outputs for the given inputs. This happens in the model in

Kohonen’s self−organizing map.

Kohonen’s Linear Vector Quantizer (LVQ) described briefly below is later extended as a self−organizing

feature map. Self−organization is also learning, but without supervision; it is a case of self−training.

Kohonen’s topology preserving maps illustrate self−organization by a neural network. In these cases, certain

subsets of output neurons respond to certain subareas of the inputs, so that the firing within one subset of

neurons indicates the presence of the corresponding subarea of the input. This is a useful paradigm in

applications such as speech recognition. The winner−take−all strategy used in ART1 also facilitates

self−organization.