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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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