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Neural
Network Theory Introduction For
a long time, the most interesting area of the science known as neural networks
was in a shadow. Only recently has it received the wide recognition it deserves,
as needs arose to process huge masses of data. In the first years of
development, neural networks were considered strategically important, and the
products, both software and hardware, were made by order, mostly, of government
and military industry. Today, neural networks are beginning to be much more
common. Neural networks are now used
in the positioning of battle units (tanks, missiles, etc.), on satellite
snapshots from space (military industry), for the prediction of the election
returns (sociology), for the detection and diagnosis of disease (medicine), and
the prediction of stocks on the basis of historical data (stock market). In
part, the neural network model emulates the operation of the brain of a human.
Its structure and the principles of operation are similar. Therefore neural net
models are capable of generalizing various sets of data, of catching hidden,
occasionally even unpredictable, dependencies, and producing outcome, which is,
in large degree, authentic. The
principle of neural net operation consists in the following: you have a set of
input data, and according to this data, a set of output data. And you can make
the supposition, that between input and output data, there is a hidden link. For
example, it is possible to assume, that there is a dependence between today's
price for fuel, and the sales volume of automobiles in one month. In the
learning process, the network is set up so as to minimize the error obtained at
any incongruity of outcome produced by the network. Processing a huge amount of
such examples, the network starts to catch dependencies of output data from
input data. There is no necessity to be a professional in the area of neural
net simulation, technical analysis and/or trading to use this software. Simply
download the necessary historical data from the Internet and start the training
mode. The neural network will then do the rest. |
