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Artificial Foreteller of Solar Flare


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 A
1&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.

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.


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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