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What is associative self-building neural network?

Associative self-building neural network is a new class of neural networks that dynamically change their structures during training. All used for train data samples are stored in the structure of the network. That means that associative self-building neural network "remembers" all data samples that were used during training session, and each fragment of data is stored only once in it.


Associative self-building neural network applies inductive way of learning. When the network is trained it feeds with descriptions of data samples. Each data sample is marked with positive or negative flag that denotes to which part of investigated phenomenon this sample belongs. The network applies inductive way of learn and compares new sample with all entered before samples and derives regularities that are peculiar to different data samplings and fixes them into network structure by means of forming new neurons and changing connections weights.


Inductive way of learning resided in associative self-building neural network differs this type of network from commonly used neural networks that realize deductive way of learning. Inductive way of learning can continue without jeopardizing that proposed neural network can be overtrained. Network size is limited only with memory capacity of available hard disks and PC processing power. The more PC has processing power the more experience can be concentrated in associative self-building neural network can be accumulated for further prediction or pattern recognition.


Accumulation of experience lead to growing of network size. At the beginning of its generation network grows very fast because of fixing new associations in its structure. As far as network gather knowledge, generation rate of new associations slow down. In saturated network each new train sample will be stored with only one neuron at top level of the network structure. This feature helps network enormously compress data.


Network size and amount of data is the second and very important problem. Associative self-building neural network can consists of tenth thousands of nodes. During recognition or prediction network should compare description of the situation with its collected knowledge to take decision. Straightforward search of data will be very time consuming. Associative self-building neural network overcomes this problem because it has sophisticated mechanism of associative search of relevant data. This mechanism activates only a fragment of the network that contains required information and not use other inactive part of the network.


Associative self-building neural network has the following advantages:

  • Inductive way of "learn" on a set of examples.
  • Insensitivity to (moderate) noise or unreliability in the data.
  • It can permanently evolve by means of use of additional trains.
  • User cannot directly change network structure (change number or neurons and connections) that reflects only data used for train.
  • Absorbs enormous amounts of data without conventional programming.
  • Neural network size is limited only by size of hard disk.
  • Provides enormously compressed data representation.
  • Associative way of data retrieval from the network.
  • Network is portable. Data is stored in a format which will not become obsolete with change of hardware.
  • Users need little training.


Where associative self-building neural network can be applied?

Associative self-building neural network can be effectively applied in those areas where conventional technologies are not able to be used because of ill-defined or very complex relationships.

It can be used for:

  • Forecasting the future changes in prices of stocks, exchange rates and commodities.
  • Solar flare prognosis
  • Risk management.
  • Fault diagnosis.
  • Medical diagnoses.
  • Tax fraud catching.
  • Oil prospecting.
  • Mineral exploration.
  • and in many other areas of human activities.

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