| There
is no need to state that solar flare prediction is very important
for mankind. Solar activity influences at nearly all aspects of
human life. Energetic solar flare can cause malfunction of low-Earth
orbiting satellites, black-outs in electric power transmission
grids, radar, and navigation, power plant shutdowns, and many
other problems. It is important to be ready to these cases by
predicting solar flares. However solar activity forecast is extremely
complex problem. It is a multi-objective problem with faint correlation
between known and used criteria based on rich variety of gamma-ray,
neutron and energetic charged particle emission, cm radio emission,
coronal hole description, coronal mass ejections (CME), solar
proton event (SPE), and others. Current approaches to predicting
of geomagnetic storms reach 20 - 40% accuracy.
We
propose a way of solar flare prediction based on associative self-building
neural network that size is limited only PC processing power and
available memory. That means user can train Artificial Foreteller
on a set of solar condition descriptions with as many these descriptions
as computer can process. Each item of the set describes solar
condition before solar flare eruption occurred (for example, one
or two days before the sun flare eruption happened). An item can
contain as many features as a user has or wants to use. Features
can be represented as a set of codes like:
Feature A - A1, A2, ... , An.,where
Feature A is a range of values, and A1, A2, ... ,
An - set of sub ranges of values.
Feature
B - B1, B2, ... , Bm.
........
Feature
Z - Z1, Z2, ... , Zs.
Based on this set of features user can describe each case of
the sun condition as a sequence of codes:
Case 1 - A1,B2, ... , Zm.
Case 2 - A9, Bw, ... , Zr.
........
Case
m - Ak, B6, ... , Zl If
there is history data about solar flares then it is possible to
generate train set of examples that describe sun condition before
eruption of solar flare. Let say that emanation of solar flare
occurred in a day after described sun condition. A set of cases
can be represented as following:
Train
Case 1 - A1, B2, ... , Zm. - positive
Train Case 2 - A9, Bw, ... , Zr.
- positive
........
Train Case m - Ak, B6, ... , Zl - negative,
- where "positive" means that the description of the
case followed solar flare eruption, and "negative" means
that solar flare eruption did not followed.
A set of cases is used for train and generating of associative
self-building neural network. During train Artificial Foreteller
processes the set to find combinations of values of different
features that are typical for positive or negative train cases.
Let's see how Artificial Foreteller processes a train set is shown
below.
These
cases are represented in neural network with corresponding nodes
and connections. It will form four main nodes that represent two
positive, and two negative cases. At the same time it will generate
three new nodes that are common for more than one case. One of
them ( A1&B2&C5&G11&K13
) belongs to two positive cases, one (A10&B21)
- belongs to two negative cases, and one node (D11&E13&G11)
belongs both positive and negative cases (Case2, Case3). As a
whole, generated network can be represented like sum of products
and dispersions :
((A1
& B2 & C5 & G11 & K13)
& D1 & E23 & F43 &
L7 & M8 & N22 & O11
)OR((A1 & B2 & C5
& G11 & K13) &
(D11 & E13 & G11)
& F33 & L8 & M19
& N21 & O17)OR
NOT((A10 & B21)
& C15 & (D11 &
E13 & G11) &
F23 & K18 & L5 & M14
& N11 & O2)OR
NOT((A10 & B21)
& C14 & D8 & E11 &
F3 & G1 & K19 & L6
& M4 & N1 & O4)
Associative
self-building neural network is not a "black box". The
formula given above can be analyzed to separate most important
features from minor ones. It can be used as a powerful tool for
data analysis.
In
example shown above the combination of features A1&B2&C5&G11&K13
is common for two cases that describe solar conditions before
eruption of solar flare. Combination of features A10&B21
is common for two cases that describe solar conditions but eruption
of solar flare was not occurred. In that case, Artificial Foreteller
will generate conclusion that solar flare would not happen. And
combination of features D11&E13&G11
is common for two cases that belong both positive and negative
descriptions. When Artificial Foreteller predicts situation it
assesses activated nodes and if activated node represents combination
of features A1&B2&C5&G11&K13
then its prognosis is positive. If a node that represents combination
of feature's values A10&B21 is activated
then Artificial Foreteller predicts that eruption of solar flare
will not happen. In case when a node D11&E13&G11
is activated Artificial Foreteller can give only assessment of
future situation. It can predict that the case can happen or not
with % probability.
User,
who trains the system, can analyze network and decide if it necessary
to keep features D, E, and G, or remove only
sub ranges D11, E13, and G11. After
that Artificial Foreteller can be trained again to check if its
prediction becomes better or not. This way user can select most
important and representative features that describe solar condition
and remove less important ones.
With help of generated neural network Artificial Foreteller will
predict that solar flare would happen in a day or not. To have
system that will provide prediction on the second day or later
after described solar condition it is required to generate and
train another network. Artificial Foreteller can have as many
network as many problems it should solved.
Artificial Foreteller Structure
Initially
network structure consists of two layers. The lowest layer is
a set of sensors. The second layer consists of receptors connected
to the sensors.
Each
feature that describes condition of the sun corresponds to a
sensor. Sensors are divided into two types - qualitative and
quantitative. Qualitative sensor transfers only signals "YES"/"NO"
to the receptor that is connected with the sensor. Signal "YES"
activates the receptor. Quantitative sensor is connected with
a set of receptors. Range of values that sensor receives is
divided into a set of sub ranges. Current version of preprocessor
can form from 3 up to 50 sub ranges. Accordingly, quantitative
sensor can combine up to 50 receptors. In that case receptor
becomes activated only if the sensor sends a signal that value
corresponds to a sub range of values that was confronted to
the receptor. Nodes of associative self-building neural network
are formed by network builder only during training sessions.
During
train neural network is formed. In its structure all derived
regularities (or combination of features) are represented with
nodes and their weights. In a mode of solar flare prediction,
entered into Artificial Foreteller description of solar condition
activates part of the neural network. Activated part of the
neural network sends signals about activation into Artificial
Foreteller of Solar Flare where they are assessed. As a result,
Artificial Foreteller of Solar Flare issues prediction if next
day will be solar flare eruption or not.
 Associative
self-building neural network can be used not only for solar
flare prediction, but for classification and data analysis.
Analysis of network structure is quite important to derive most
significant features. Associative self-building neural network
works over and accumulates vast amount of information about
critical solar features.
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Artificial
Foreteller is a new powerful system that demonstrates extraordinary
ease of use for prediction, and classification situations.
Artificial Foreteller analyses current input values and
predicts future situation with solar flare. That means Artificial
Foreteller tries to predict what
will happen, and not what already happened.
As it well known human being can operate with not more than
7 criteria at the same time. As distinct from human being
Artificial Foreteller is able to operate as many different
criteria and features as it has been trained.
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Artificial
Foreteller can "remember" thousands of solar conditions
in its neural network and use them for analysis of the current
one. It forms prediction based on derived from train data
set of regularities and analogies among analyzed and known
situations.
Artificial
Foreteller was written in VC++ and works under MS Windows98/me/NT/2000/XP.
A virtual memory storage was developed to store neural network
and let the system to create network with size more than size
of PC central memory. With help of virtual memory storage
neural network became portable and can be transferred among
different PCs.
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Interested
parties can contact us at:e-mail
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