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WORKING ENVIRONMENT:
Operating System :- WINDOWS XP
Environment Used :- MATLAB 7.0.1

PROJECT INFORMATION:
Our project is basically “compressing a “.bmp” image using BackPropagation algorithm in MultiLayered Neural Network”. This is a research topic which we are trying to implement in MATLAB 7.0.1.
A neural network is a powerful data modeling tool that is able to capture and represent complex input/output relationships. The motivation for the development of neural network technology stemmed from the desire to develop an artificial system that could perform "intelligent" tasks similar to those performed by the human brain. Neural networks resemble the human brain in the following two ways:
1. A neural network acquires knowledge through learning.
2. A neural network's knowledge is stored within inter-neuron connection strengths known as synaptic weights.
The true power and advantage of neural networks lies in their ability to represent both linear and non-linear relationships and in their ability to learn these relationships directly from the data being modeled. Traditional linear models are simply inadequate when it comes to modeling data that contains non-linear characteristics.


• Three layers, one input layer, one output layer and one hidden layer.

• Compression is achieved by designing the value of K, the number of neurons at the hidden layer, less than that of neurons at both input and output layers.
• The input image is split up into blocks or vectors of 8*8, 4*4 or 16*16 pixels.
• Coupling weights connected to each neuron at the hidden layer can be represented by {wji, j = 1, 2, ... K and i = 1, 2, ... N}, which can also be described by a matrix of K*N.
• From the hidden layer to the output layer, the connections can be represented by {w’ij : } which is another weight...