Network Selection

Because all artificial neural networks are based on the concept of neurons, connections and transfer functions, there is a similarity between the different structures or architectures or neural networks. The majority of the variations stems from the various learning rules and how those rules modify a network's typical topology. The following sections outline some of the most common artificial neural networks. They are organized in very rough categories of application. these categories are not meant to be exclusive, they are merely meant to seperate out some of the confusion over networks architectures and their best matches to specific applications.

Basically, most applications of neural networks fall into the follwing five categories:

    1. prediction
    2. classification
    3. data association
    4. data conceptualization
    5. data filtering

Network Type

Networks

Use for Network

Prediction
Back-propagation
Delta Bar Delta
Extended Delta Bar Delta
Directed Random Search
Higher Order Neural Networks
Self-organizing map into Back-propagation

Use input values to predict some output (e.g. pick the best stocks in the market, predict weather, identify people with cancer risks etc.)

Classification
Learning Vector Quantization
Counter-propagation
Probabalistic Neural Networks

Use input values to determine the classification (e.g. is the input the letter A, is the blob of video data a plane and what kind of plane is it)

Data Association
Hopfield
Boltzmann Machine
Hamming Network
Bidirectional associative Memory
Spation-temporal Pattern Recognition

Like Classification but it also recognizes data that contains errors (e.g. not only identify the characters that were scanned but identify when the scanner isn't working properly)

Data Conceptualization
Adaptive Resonance Network
Self Organizing Map

Analyze the inputs so that grouping relationships can be inferred (e.g. extract from a database the names of those most likely to buy a particular product)

Data Filtering
Recirculation

Smooth an input signal (e.g. take the noise out of a telephone signal)

Table 3 Network Selector Table

Table 3 shows the differences between these network categories and shows which of the more common network topologies belong to which primary category. this chart is intended as a guide and is not meant to be all inclusive. While there are many other network derivations, this chart only includes the architectures explained within this section of this report. Some of these networks, which have been grouped by application, have been used to solve more than one type of problem. Feedforward back-propagation in particular has been used to solve almost all types of problems and indeed is the most popular for the first four categories. the next five subsections describe these five network types.

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