neural network
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NEURAL NETWORK
Submitted by : Abhishek Sasan(500901515)Laleet Grover()Munish Kumar(500901505)
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Definition Examples Types of Neural Networks Selection of NN Areas where NN is useful Applications Advantages Limitations SNNS
Definition
A neural network is a computational method inspired by studies of the brain and nervous systems in biological organisms.
A Computing system made of a number of simple, highly interconnected processing elements, which process information by their dynamic state response to external input.
Example :Single Neuron
Example :Three Layers Neural Net
Neural Network
They can be distinguished by:their type (feed forward or feed back)their structure the learning algorithm they use
Types of Neural Network
Single Layer Feed forward Network
Multi -Layer Feed forward Network
Feed Back Network
Selection of Neural Nets
Perceptron
Multi-Layer-Perceptron
Back propagation Net
Hopfield Net
Kohonen Feature Map
How Do Neural Networks Work ?
The output of a neuron is a function of the weighted sum of the inputs plus a bias
Neuron
w1i1
w2i2
i3 w3
Output = f(i1w1 + i2w2 + i3w3 + bias)
Bias
Areas where Neural Net May be Useful
Pattern association
Pattern classification
Regularity detection
Image processing
Speech analysis
Optimization problems
Three Main Applications
Concurrent simulation, where results of an ANN model are compared with results of a less realistic but validated common model to avoid a non expected behavior of the Neural-Net.
ANN as sub-components of a global model, to model subsystems that would be hard to model commonly because of a lack of understanding.
Adaptive models, "models that can learn", according to an error feedback such model would be able to adapt runtime to situations that hasn't been taken into account.
Why Use Neural Networks
Ability to learn : NN’s figure out how to perform their function on their
own
Determine their function based only upon sample inputs
Ability to generalize
i.e. produce reasonable outputs for inputs it has not been taught how to deal with
Advantages : Neural Network
Handle partial lack of system understanding Create adaptive models (models that can learn)
Limitations
The operational problem encountered when attempting to simulate the parallelism of neural networks
Instability to explain any results that they
obtain
Neural Network Software
Neural network software is used to stimulate, research, develop and apply artificial neural networks, biological neural networks
Simulators usually have some form of built- in visualization to monitor the training process
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