INTRODUCTION TO NEURAL NETWORK
INTRODUCTION
The study of brain is an interesting area since a long time. With advancement in thefield of electronics and computer science,
it was the assumed that we can use this natural way of this thinking process of brain to design some artificial intelligence system.
The first step toward artificial intelligence came into existence in 1943 when Warren McCulloch, a neurophysiologist, and a mathematician, Walter Pitts,
wrote a paper on how neurons work. Mathematical analysis has solved some of the mysteries posed by the new models but
has left many questions for future investigations. There is no need to say, the study of neurons, their interconnections,and
their role as the brain's elementary building blocks is one of the most dynamic and important research fields in modern world
of electronics and computer science.
Artificial neural networks commonly referred as the neural networks are the information or signal processing mathematical model
that is based on the biological neuron.A neural network is a complex structure which consist a group of interconnected neurons
which provides a very exciting alternatives for complex problem solving and other application which can play important role in today's
computer science field so researchers from the different discipline are designing the artificial neural networks to solve
the problems of pattern recognition, prediction, optimization, associative memory and control.
In this paper we have presented the basic study of the artificial neural network, its characteristics and its applications
ARTIFICIAL NEURAL NETWORKS
In electronics engineering and related fields, artificial neural networks (ANNs) are mathematical or computational models that are inspired
by ahuman's central nervous system (in particular the brain) which is capable of machine learning as well as pattern recognition.
Where as animal's nervous system ismore complexthan the human so the system designed like this will be able to
solve more complex problems. Artificial neural networks are generally presented as systems of highly interconnected "neurons"
which can compute values from inputs.
Neural Network is just like a website network of inter-connected neurons which can be millions in number.
With the help of these interconnected neurons all the parallel processing is being done
in body and the best example of Parallel Processing is human or animal'sbody.
Currently,artificial neural networks are the clustering of the primitive artificial neurons.
This clustering occurs by creating layers which are then connected to one another.
How these layers connect is the other part of the "art" of engineering networks to resolve the complex problems of the real world.
So neural networks, with their stronger 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.
