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Artificial
Foreteller of Stocks and Commodities is only one of the examples that
demonstrates abilities of associative self-building neural networks to
solve very complex problems as prediction, classification, repair, and
diagnosis. To develop new systems that will process any other type of data
it is only need to change preprocessor and GUI in system structure
described for Artificial Foreteller. Network Builder and Interpreter will
remain the same. Below there are several examples of the network
implementation.
Mineral and oil exploration
Preprocessor receives on its
entry presence or absence of a mineral occurrence, geologic or formation
characteristics and other relevant information that describe known mineral
deposits or oilfields. Based on this information the system builds
associative self-building neural network. Then user enters into the system
description of area where there believe exist requested mineral deposit or
oilfield. Use of the system with proper trained associative self-building
neural network can greatly reduce mineral or oil prospecting risks and of
course reduce prospecting cost.
Solar Flare Prognosis
Preprocessor
gets a lot of data pertaining to gamma-ray, neutron, energetic charged
particle emission, and other criteria that has been observed from solar
flares. During train session all known cases with solar flares should be
entered into Associative self-building neural network. Then trained network
can be easily used for prediction future solar flares.
Tax fraud catching
Preprocessor
should have judgmental benefits of an expert systems to prepare tax data
for input into Associative neural network. Files with tax fraud cases that
were found can be used as train data. During prediction of tax fraud a
queer report can be processed by associative self-building neural network
to make relevant decision.
Explosive staff detection
Preprocessor
should get a set of data collected from different sensors that describe situation
with explosive staff detection. During train session associative
self-building neural network process available data about such kind of
cases and stores them in its structure. At the same time it derives
regularities and associations that are common for known cases and stores
them in its structure too. During employment associative self-building
neural network compares current set of data collected from sensors that
describes situation with its experience and makes conclusion. In contrast
to human being and dogs the system is not tired of analysing at this kind
of staff.
As it can be easily seen that current approaches can
be used for many different data domain.
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