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sujith.boss01 · 10-04-2017, 09:24 PM

Submitted By :-
Vikas Sharma

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Introduction to Artificial Neural Networks
Neural networks : Introduction
Neural network: information processing paradigm inspired by biological nervous systems, such as our brain
Structure: large number of highly interconnected processing elements (neurons) working together
Like people, they learn from experience (by example)
Neural networks : Introduction
Neural networks are configured for a specific application, such as pattern recognition or data classification, through a learning process
In a biological system, learning involves adjustments to the synaptic connections between neurons
same for artificial neural networks (ANNs)
A new sort of computer
What are (everyday) computer systems good at.. and not so good at?
Where can neural network systems help
when we can't formulate an algorithmic solution.
when we can get lots of examples of the behavior we require.
learning from experience
when we need to pick out the structure from existing data.
Inspiration from Neurobiology
A neuron: many-inputs / one-output unit
output can be excited or not excited
incoming signals from other neurons determine if the neuron shall excite ("fire")
Synapse concept
The synapse resistance to the incoming signal can be changed during a "learning" process [1949]
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.
A simple perceptron
It s a single-unit network
Change the weight by an amount proportional to the difference between the desired output and the actual output.
Wi = * (D-Y).Ii
Example: A simple single unit adaptive network
The network has 2 inputs, and one output. All are binary. The output is
1 if W0I0 + W1I1 > 0
0 if W0I0 + W1I1 0
We want it to learn simple OR: output a 1 if either I0 or I1 is 1.
Artificial Neural Networks
Adaptive interaction between individual neurons
Power: collective behavior of interconnected neurons
Evolving networks
Continuous process of:
Evaluate output
Adapt weights
Take new inputs
ANN evolving causes stable state of the weights, but neurons continue working: network has learned dealing with the problem
Learning
From experience: examples / training data
Strength of connection between the neurons is stored as a weight-value for the specific connection
Learning the solution to a problem = changing the connection weights
Learning performance
Learning Paradigms:
Unsupervised
Competitive Learning
Reinforcement learning
Backpropagation
Unsupervised learning
No help from the outside
No training data, no information available on the desired output
Learning by doing
Example : -
Competitive learning: example
In this type, it is generally Winner takes all concept.
only update weights of winning neuron
Back propagation
Desired output of the training examples
Error = difference between actual & desired output
Change weight relative to error size
Calculate output layer error , then propagate back to previous layer
Improved performance, very common!
Where are NN used?
Recognizing and matching complicated, vague, or incomplete patterns
Data is unreliable
Problems with noisy data
Prediction
Classification
Data association
Data conceptualization
Filtering
Planning
Applications
Prediction: learning from past experience
pick the best stocks in the market
predict weather
identify people with cancer risk
Classification
Image processing
Predict bankruptcy for credit card companies
Risk assessment
Applications
Recognition
Pattern recognition: SNOOPE (bomb detector in U.S. airports)
Character recognition
Handwriting: processing checks
Data association
Not only identify the characters that were scanned but identify when the scanner is not working properly
Applications
Data Conceptualization
infer grouping relationships e.g. extract from a database the names of those most likely to buy a particular product.
Data Filtering
e.g. take the noise out of a telephone signal, signal smoothing
Planning
Unknown environments
Sensor data is noisy
Fairly new approach to planning

Strengths of a Neural Network
Power: Model complex functions, nonlinearity built into the network
Ease of use:
Learn by example
Very little user domain-specific expertise needed
Intuitively appealing: based on model of biology, will it lead to genuinely intelligent computers/robots?
Neural networks cannot do anything that cannot be done using traditional computing techniques, BUT they can do some things which would otherwise be very difficult.
General Advantages
Advantages
Adapt to unknown situations
Robustness: fault tolerance due to network redundancy
Autonomous learning and generalization
Disadvantages
Not exact
Large complexity of the network structure
Future of Neural Networks
Most of the reported applications are still in research stage
No formal proofs, but they seem to have useful applications that work

vijay2020 · 10-04-2017, 09:24 PM

Most people when asked if they think computers could ever become sentient quickly respond no and refer to the fact that computers are unable to learn. However, Neural Networks seems to do just that.

Neural Networks encompass a diverse set of computational models, which share a set of simple underlying characteristics. Inspired by the computational style of biological systems, a Neural Network can be viewed as an assembly of simple, interconnected processing units (neurons) acting in parallel, which communicate to each other using unidirectional connections.

Neural networks are distinguished from other computer and mathematical techniques by their design motivation. They are processing devices, that can be algorithms or actual hardware that are modeled after the functioning of human brain. Most Neural Networks have some sort of training rule whereby the weights of connections are adjusted on the basis of presented patterns. In other words, Neural Networks learn from examples, just like children learn to recognize dogs from examples of dogs and exhibit some structural capability for generalization.

The most significant aspects of Neural Networks are that they allow the computer to learn and they have the potential for parallelism. This means that they allow the computer to solve multiple problems at a time.

Neural Networks can perform any variety of tasks just as any regular computer. They are of greatest use in computing problems where the input does not follow clean strict rules but instead has an overall pattern. Neural Networks have applications in diverse areas like interpretation, prediction, diagnosis, planning, monitoring, debugging, repair, instruction, control, categorization and pattern recognition. Thus Neural Networks is an exponentially growing area of real- time applications of the new era.

http://pptpdf.net/subcategory.php?categ=...TWORKS.pdf

skpbravo · 10-04-2017, 09:24 PM

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Artificial Neural Networks

INTRODUCTION
During the infancy of the development of Neural Networks technology, one thing that excited people s interest was its analogy to biological systems. Even though not all has been understood about the learning processes of human neural systems, Artificial Neural Networks (ANN) have, without a doubt, provide the solution to problems in different application areas [1]. The brain is a highly complex, nonlinear and parallel information processing system. It consists of about one hundred billion neural cells, each connected to about 10,000 neighboring neurons and receiving signals from there. The brain routinely accomplishes perceptual recognition tasks (e.g., recognizing a familiar face in a scene) in about 100-200 msec. The neuron, the basic information processing element (PE) in the central nervous system plays a very important and diverse role in human sensory processing, control and cognition. The brain is able to do complex tasks by its ability to learn from experience. An Artificial Neural network is designed to model the working of human brain.
The ANN is usually implemented using electronic components (digital & analog) and/or simulated on a digital computer. It employs massive interconnection of simple computing cells called neurons or processing elements (PE) It resembles the brain in two ways:

Knowledge is acquired by the network through learning process,
Inter neuron connection strength (synaptic weights) are responsible for storing the knowledge.
The way the synaptic weights change is what makes the design of ANNs. Such an approach is close to linear adaptive filter theory, which is well established and is used in many diverse fields such as communication, control, sonar, radar, and biomedical engineering.

balaji_satish · 10-04-2017, 09:24 PM

Just as life attempts to understand itself better by modeling it, and in the process create something new, so Neural computing is an attempt at modeling the workings of a brain and this presentation is an attempt to understand the basic concept of artificial neural networks.

In this paper, a small but effective overall content of artificial neural networks is presented . .First,the history of Neural Networks which deals with the comparative study of how vast the Neural Networks have developed over the years is presented. Next, having known what exactly is a neural network with the help of a MLP model, we proceed to next session: resemblance with brain where in the comparison between brain and neural networks as well as neurons and perceptrons are made with the help of figures. The most basic component of a neural network is the perceptron, which is called the artificial neuron, is studied and depicted in the Structure of a Neural Network section which is followed by architecture. The most important concept of the neural networks are its wide range of its applications, a few of which will be dealt in the consequent sections and then its limitations. The main question of interest to us would be What will be the future of Neural Networks, Will it survive or will it rule us? This section leads us to a brief conclusion and we end the paper with the references.

http://pptpdf.net/subcategory.php?categ=...TWORKS.pdf

sumit kumar · 10-04-2017, 09:24 PM

AJINA VAHID · 10-04-2017, 09:24 PM