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INTRODUCTION
BACKGROUND:
Many task which seem simple for us, such as reading a handwritten note or recognizing a face, are difficult task for even the most advanced computer. In an effort to increase the computer ability to perform such task, programmers began designing software to act more like the human brain, with its neurons and synaptic connections. Thus the field of “Artificial neural network” was born. Rather than employ the traditional method of one central processor (a Pentium) to carry out many instructions one at a time, the Artificial neural network software analyzes data by passing it through several simulated processos which are interconnected with synaptic like “weight”
Once we have collected several record of the data we wish to analyze, the network will run through them and “learn ” the input of each record may be related to the result. After training on a few doesn’t cases the network begin to organize and refines its on own architecture to feed the data to much the human brain; learn from example.
This “REVERSE ENGINEERING” technology were once regarded as the best kept secret of large corporate, government an academic researchers.
The field of neural network was pioneered by BERNARD WIDROW of Stanford University in 1950’s.
Why would anyone want a `new' sort of computer?
What are (everyday) computer systems good at... .....and not so good at?
Good at Not so good at
Fast arithmetic Interacting with noisy data or data from the environment
Doing precisely what the programmer programs them to do Massive parallelism
Fault tolerance
Adapting to circumstances
Where can neural network systems help?
• where we can't formulate an algorithmic solution.
• where we can get lots of examples of the behaviour we require.
• where we need to pick out the structure from existing data.
What is a neural network?
Neural Networks are a different paradigm for computing:
• Von Neumann machines are based on the processing/memory abstraction of human information processing.
• Neural networks are based on the parallel architecture of animal brains.
Neural networks are a form of multiprocessor computer system, with
• Simple processing elements
• A high degree of interconnection
• Simple scalar messages
• Adaptive interaction between elements
Artificial neural network (ANNs) are programs designed to solve any problem by trying to mimic structure and function of our nervous system. Neural network are based on simulated neurons. Which are joined together in a variety of ways to form networks. Neural network resembles the human brain in the following two ways: -
A neural network acquires knowledge through learning
A neural network’s knowledge is stored with in the interconnection strengths known as synaptic weight.
Neural network are typically organized in layers. Layers are made up of a number of interconnected ‘nodes’, which contain an ‘activation function’. Patterns are presented to the network via the ‘input layer’, which communicates to one or more ‘hidden layers’ where the actual processing is done via a system of weighted ‘connections’. The hidden layers then page link to an ‘output layer’ where the answer is output as shown in the graphic below.

Each layer of neural makes independent computation on data that it receives and passes the result to the next layers(s). The next layer may in turn make independent computation and pass data further or it may end the computation and give the output of the overall computation .The first layer is the input layer and the last one, the output layer. The layers that are placed within these two are the middle or hidden layers.
A neural network is a system that emulates the cognitive abilities of the brain by establishing recognition of particular inputs and producing the appropriate output. Neural networks are not “hard-wired” in particular way; they are trained using presented inputs to establish their own internal weights and relationships guided by feedback. Neural networks are free to form their own internal working and adapt on their own.
Commonly neural network are adjusted, or trained so that a particular
input leads to a specific target output Target


There, the network is adjusted based on a comparison of the output and the
target, until the network output matches the target. Typically many such input/target pairs are used to train network.
Once a neural network is ‘trained’ to a satisfactory level it may be used as an analytical tool on other data. To do this, the user no longer specifies any training runs and instead allows the network to work in forward propagation mode only. New inputs are presented to the input pattern where they filter into and are processed by the middle layers as though training were taking place, however, at this point the output is retained and no back propagation occurs.
The structure of Nervous system
Nervous system of a human brain consists of neurons, which are interconnected to each other in a rather complex way. Each neuron can be thought of as a node and interconnection between them are edge, which has a weight associates with it, which represents how mach the tow neuron which are connected by it can it interact. Node (neuron) (interconnection)
Functioning of A Nervous System
The natures of interconnections between 2 neurons can such that – one neuron can either stimulate or inhibit the other. An interaction can take place only if there is an edge between 2 neurons. If neuron A is connected to neuron B as below with a weight w, then if A is stimulated sufficiently, it sends a signal to B. The signal depends on

w
A B
The weight w, and the nature of the signal, whether it is stimulating or inhibiting. This depends on whether w is positive or negative. If its stimulation is more than its threshold. Also if it sends a signal, it will send it to all nodes to which it is connected. The threshold for different neurons may be different.
If many neurons send signal to A, the combined stimulus may be more than the threshold.
Next if B is stimulated sufficiently, it may trigger a signal to all neurons to which it is connected.
Depending on the complexity of the structure, the overall functioning may be very complex but the functioning of individual neurons is as simple as this. Because of this we may dare to try to simulate this using software or even special purpose hardware.
Major components Of Artificial Neuron
This section describes the seven major components, which make up an artificial neuron. These components are valid whether the neuron is used for input, output, or is in one of the hidden layers.
Component 1. Weighing factors:
A neuron usually receives many simultaneous inputs. Each input has its own relative weight, which gives the input the impact that it needs on the processing elements summation function. These weights perform the same type of function, as do the varying synaptic strengths of biological neurons. In both cases, some input are made more important than others so that they have a greater effect on the processing element as they combine to produce a neuron response.
Weights are adaptive coefficients within the network that determine the intensity of the input signal as registered by the artificial neuron. They are a measure of an input’s connection strength. These strengths can be modified in response to various training sets and according to a network’s specific topology or through its learning rules.
Component 2. Summation Function:
The first step in a processing element’s operation is to compute the weighted sum of all of the inputs. Mathematically, the inputs and the corresponding weights are vectors which can be represented as (i1, i2,………….in) and (w1, w2,……………….wn). The total input signal is the dot, or inner, product of these two vectors. This simplistic summation function is found by multiplying each component of the i vector by the corresponding component of the w vector and then adding up all the products. Input1 = i1*w1, input2=i2*w2, etc., are added as input1+input2+………..+inputn. The result is a single number, not a multi-element vector.
Geometrically, the inner product of two vectors can be considered a measure of their similarity. If the vector point in the same direction, the inner product is maximum; if the vectors points in opposite direction (180 degrees out of phase), their inner product is minimum.
The summation function can be more complex than just the simple input and weight sum of products. The input and weighting coefficients can be combined in many different ways before passing on to the transfer function. In addition to a simple product summing, the summation function can select the minimum, maximum, majority, product, or several normalizing algorithms. The specific algorithm for combining neural inputs is determined by the chosen network architecture and paradigm.