Research and Analysis Software In Stocks & Bonds Investment Directory

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Associative Neuron Systems In Stock Futures Funds Directory

 

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.

 

Website: http://avis-c.gluk.org/