artificial neural networks
The computational changes in the last several decades have brought growth to new
technologies. One of these technologies is artificial neural networks (ANNs). Over the years,
ANNs have given various solutions to the industry. Designing and implementing intelligent
systems
have become an important activity for the innovation and development of better
products for human life. Examples might include the case of the implementation of artificial life
and giving solution to interrogatives that linear systems are not able to resolve .An ANN is a
mathematical model to deal with the information that is inspired by the way biological nervous
system, such as the brain, performs in information processing. A key element in this model is the
structure of this novel information processing system. The system is adaptive and is consisting of
a large number of interrelated processing elements (neurons) working together to solve specific
problems.
Artificial Neural Networks can be viewed as parallel and distributed processing systems which
consists of a huge number of simple and massively connected processors.
Biological neural network
The concept of ANN is basically introduced from the subject of biology, where neural
network plays a important and key role in human body. In human body work is done with the
help of neural network. Neural Network is just a web of inter connected neurons which are
millions and millions in number. With the help of this interconnected neurons all the parallel
processing is done in human body and the human body is the best example of Parallel
Processing. A neuron is a special biological cell that process information from one neuron to
another neuron with the help of some electrical and chemical change. It is composed of a
cell body or soma and two types of out reaching tree like branches: the axon and the
dendrites. The whole process of receiving and sending signals is done in particular manner
like a neuron receive signals from other neuron through dendrites. The Neuron send
signals at spikes of electrical activity through a long thin stand known as an axon and an
axon splits this signals through synapse and send it to the other neurons
artificial neural network module
Artificial neural network is a massively parallel distributed processor that has a natural
propensity for storing experiential knowledge and making it available for use. It resembles the
brain in two respects:
- Knowledge is acquired by the network through a learning process.
- Interconnection strengths known as synaptic weights are used to store the knowledge.
The basic unit of neural network ,the artificial neuron simulating a work of the neuron in human
brain.
The neuron consist of some inputs emulating dendrites of the biological neuron, a summation
model , an activation function and one output emulating an axon of biological neurons.
The importance of a particular input can be intensified by weight that simulate biological
neurons synapses. Then the input signals are multiplied by values of weight and next the result
are added in the summation part , the sum is send to the activation part where is processed by the
activation function , thus we obtained neurons output for input signal ‘x’.
Artificial neural network advantages
Neural networks, with their remarkable ability to derive meaning from complicated or
imprecise data, can be used to extract patterns and detect trends that are too complex to
be noticed by either humans or other
computer techniques. A trained neural network can be thought of as an "expert" in the
category of information it has been given to analyze. Other advantages include:
1. Adaptive learning: An ability to learn how to do tasks based on the data given for
training or initial experience.
2. Self-Organization: An ANN can create its own organization or representation of the
information it receives during learning time.
3. Real Time Operation: ANN computations may be carried out in parallel, and special
hardware devices are being designed and manufactured which take advantage of this capability.
4. Fault Tolerance : Partial destruction of a network leads to the corresponding
degradation of performance. However, some network capabilities may be retained even
with major network damage.