Artificial Neural Network (U)

8/3/2019 Artificial Neural Network (U)

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Artificial Neural Network(ANN)



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Neural Networks (NN):- have remarkable ability to drive meaning from

complicated or imprecise data.

“knowledge acquisition tools” that learn from

examples

Neural Networks are used for:-

pattern recognition (objects in images, voice,medical diagnostics for diseases, etc.).

exploratory analysis (data mining).

predictive models and control.

Definition & Area of Application



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Inspired by natural decision making structures(real nervous systems and brains)

If you connect lots of simple decision makingpieces together, they can make more complex

decisions Compose simple functions to produce complex

functions

Neural networks: Take multiple numeric input variables Produce multiple numeric output values Normally threshold outputs to turn them into discrete

values.

Neural Networks



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What is an artificial neural

network?

An Artificial Neural Network (ANN) is an

information processing paradigm that is inspired by

the way biological nervous systems, such as the brain,

process information.The key element of this paradigm is the novel

structure of the information processing system.

It is composed of a large number of highlyinterconnected processing elements (neurons)

working in union to solve specific problems.



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Cont.

The term Neural network is in fact a biological

term which is collection of neuron, the tiny

cell are brain is comprised of . A network can

consists of few to a few billion neurons

connected in an array of different methods.

ANN’s attempt to model these biological

structures both in architectures and operations



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Advantage and Disadvantage

Advantages:

A neural network can perform tasks that a linear program cannot.

When an element of the neural network fails, it can continuewithout any problem by their parallel nature.

A neural network learns and does not need to bereprogrammed.

It can be implemented without any problem.

Disadvantages: The neural network needs training to operate.

Requires high processing time for large neural networks.



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Neural Network Architectures

Feed-forward networks:-

Feed-forward ANNs allow signals to travel one way only; from input to

output. There is no feedback (loops) i.e. the output of any layer does not

affect that same layer. Feed-forward ANNs tend to be straight forwardnetworks that associate inputs with outputs.

They are extensively used in pattern recognition.



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Cont.

Recurrent /Feedback networks:-

Feedback networks can have signals travelling in both directions byintroducing loops in the network. Feedback networks are very powerfuland can get extremely complicated. Feedback networks are dynamic; their'state' is changing continuously until they reach an equilibrium point.

They remain at the equilibrium point until the input changes and a newequilibrium needs to be found.

Feedback architectures are also referred to as interactive or recurrent,although the latter term is often used to denote feedback connections insingle-layer organizations.



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Learning/Training of artificial

neural networks

A neural network has to be configured suchthat the application of a set of inputs produces(either 'direct' or via a relaxation process) the

desired set of outputs. Various methods to setthe strengths of the connections exist. One wayis to set the weights explicitly, using a prioriknowledge. Another way is to 'train' the neural

network by feeding it teaching patterns andletting it change its weights according to somelearning rule.



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Cont.

We can categorize the learning situations in two distinct sorts. These are:

Supervised learning or Associative learning in which the network is trainedby providing it with input and matching output patterns. These input-outputpairs can be provided by an external teacher, or by the system whichcontains the neural network (self-supervised).



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Cont.

Unsupervised learning or Self-organization in which an (output)unit is trained to respond to clusters of pattern within the input. Inthis paradigm the system is supposed to discover statistically salientfeatures of the input population.

Unlike the supervised learning paradigm, there is no a priori set ofcategories into which the patterns are to be classified; rather the

system must develop its own representation of the input stimuli.



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Cont.

Reinforcement Learning This type of learning may be consideredas an intermediate form of the above two types of learning. Here thelearning machine does some action on the environment and gets afeedback response from the environment.

The learning system grades its action good (rewarding) or bad(punishable) based on the environmental response and accordinglyadjusts its parameters. Generally, parameter adjustment iscontinued until an equilibrium state occurs, following which there willbe no more changes in its parameters. The self-organizing neurallearning may be categorized under this type of learning.



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Application

Neural Network applicability to real world business

problem like sales forecasting, customer research,

marketing, credit assignment evaluation etc

Modeling and diagnosing the cardiovascular system Application in biomedical researches n medicine and

instant physician

Robotics Application Electronic Noses

Artificial Neural Network (U)

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