NEURAL NETWORK

Submitted by : Abhishek Sasan(500901515)Laleet Grover()Munish Kumar(500901505)

Content

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