- The artificial neural network or ANN (sometimes
also called as just neural network) is a mathematical model that has got its
inspiration from the biological neural networks.
- This network is supposed to
consist of several artificial neurons that are interconnected.
- This model works
with a connectionist approach for computing and thus processes information
based up on this only.
- In a number of cases, the neural network can act as an
adaptive system that has the ability of making changes in its structure while
it is in some learning phase.
- These networks are particularly used in searching
patterns in data and for modeling the complex relationships that exist between
the outputs and inputs.
- An analogy to artificial neural network is the neuron
network of the human brain.
- In an ANN, the artificial
nodes are termed as the neurons or sometimes as neurodes or units or the ‘processing
elements’.
- They are interconnected in
such a way that they resemble a biological neural network.
- Till now, no formal
definition has been given for the artificial neural networks. - These processing
elements or the neurons show a complex global behavior.
- The connections between
the neurons and their parameters is what that determines this behavior.
- There
are certain algorithms that are designed for altering the strength of these
connections in order to produce the desired flow of the signal.
- The ANN
operates up on these algorithms.
- As in biological neural networks, in ANN also
functions are performed in parallel and collectively by the processing units.
- Here, there is no delineation of the tasks that might be assigned to different
units.
- These neural networks are employed in various fields such as:
- Statistics
- Cognitive
psychology
- Artificial
intelligence
- There are other neural network models that emulate
biological CNS and are part of the following:
- Computational
neuroscience
- Theoretical
neuroscience
- The modern software
implementation of the ANNs prefers a more practical approach than biologically
inspired approach.
- This practical approach is based up on the signal processing
and statistics. The former approach has been largely abandoned.
- Many times parts
of these neural networks serve as components for the other larger systems that
are a combination of non – adaptive and adaptive elements.
- Even though a more
practical approach for solving the real world problems is the latter one, the
former has more to do with the connectionist models of the traditional
artificial intelligence.
- Well the common thing between them is the principle of
distributed, non – linear, local and parallel processing and adaptation.
- A
paradigm shift was marked by the use of neural networks during the late
eighties.
- This shift was from the high level artificial intelligence (expert
systems) to low level machine learning (dynamical system).
- These models are
very simple and define functions such as:
f: X à Y
- Three types of parameters are used
for defining an artificial neural network:
a) The
interconnection pattern between neuron layers
b) The
learning process
c) The
activation function
- The second parameter updates the
weights of the connections and the third one converts the weighted input in to
output.
- Learning is the thing that has attracted many towards it.
- There are 3
major learning paradigms that are offered by ANN:
- Supervised
learning
- Un –
supervised learning
- Reinforcement
learning
- Training a network requires
selecting from a set of models that would best minimize the cost.
- A number of
algorithms are available for training purpose where gradient descent is
employed by most of the algorithms.
- Other methods available are simulated
annealing, evolutionary methods and so on.
