|
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.
Please
send any comments or suggestions by e-mail
|
