artificial neural network seminars report

[seminar class]

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ARTIFICIAL NEURAL NETWORK
Simplified diagram of connecting neurons
How did Artificial Neural network develop?
 Seeing the billions of interconnections in the human brain, and the way the human brain recognizes different patterns, it was felt that there was a need to simulate the brain.
Model of Neuron
 BIOLOGICAL NEURON
 ARTIFICIAL NEURON
 BIOLOGICAL NEURON
Three major components of biological neuron are :
 Cell body
 Dendrites
 Axon
At the one end of the neuron

there are


a multitude of tiny filaments

called

Dendrites

dendrites
join together


To form larger branches and trunks
where they attach to cell body
At the other end of the neuron
is a


Single filament leading
out of the cell body
called the axon

Axon

Extensive branching end
links in its far end
called axon terminals

Dendrites represented as inputs to the
neuron
Axon Neuron’s output

Each neuron has

Many inputs through only one output its multiple dendrites through its single axon
Synapse
each branch of the axon meeting exactly one
dendrite of another cell
Synaptic gap Gap between the axon
terminals and dendrites of
another cell
Distance 50 and 200 Angstroms
 Connections between neurons are formed at synapses
Axon of a neuron synaptic gap Dendrite of
another neuron
Neurons

Information processors
Communications between neurons

How do they take place?
• Communication takes place with the help of electrical
signals
Signals are sent through
the axon of one neuron
To the dendrites of other neurons
 Even then the brain has very less difficulty in correctly and immediately recognizing patterns or objects.
 the crucial difference therefore lies not in the essential speed of processing but in the organization of processing.
 The key is the notion of massive parallelism or connectionism.
 The processing tasks in the brain are distributed among 10^11 -10^12 elementary nerve cells called neurons.
ARTIFICIAL NEURON
NEURAL NETWORK REPRESENTATION
 An ANN is composed of processing elements called or perceptrons, organized in different ways to form the network’s structure.
Processing Elements
 An ANN consists of perceptrons. Each of the perceptrons receives inputs, processes inputs and delivers a single output.
 Mathematical representation
 The neuron calculates a weighted sum of inputs and compares it to a threshold. If the sum is higher than the threshold, the output is set to 1, otherwise to -1.
BASIC TYPE OF APPLICATIONS
 Process control
 Vehicle control
 Forecasting and prediction
 Financial applications
Advantages
 A neural network can perform a task that a linear program cannot.
 When an element in neural network fails, it can continue without any problem by their parallel nature.
 A neural network learns and does not need to be reprogrammed.
 It can be implemented with any application.
 It can be implemented without any problem.
DISADVANTAGES
 The neural network needs training to operate.
 The architecture of a neural network is different from the architecture of microprocessors therefore needs to be emulated.
 Requires high processing time for large neural networks.
CONCLUSION
 The ability of neural networks to learn and generalize in addition to their wide range of applicability makes them very powerful tools.
 There is no need to understand the internal mechanisms of that task.
 They are also used for the real time systems.
 Finally I would like to state that even though neural networks have huge potential we will only get the best of them when they are integrated with computing, fuzzy logic and so on.