17-06-2013, 04:55 PM
ADNANCE CONTROL TECHNOLOGY AND APPLICATIONS
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HISTORICAL SYNOPSIS
Neural networks came into picture before the advent of computers but it appears to be a recent development.
As this field required more financial and professional support, minimal researches have been made.
Minsky and Papert were the two pioneers who developed a convincing thesis which overcame the limitations proposed by others.
Warren McCulloch and Walter Pits were the two neurophysiologists who developed the first artificial neuron in 1943.
Artificial Neural Networks (ANN)
Neural network inspired by biological nervous systems, such as our brain.
The key element of this idea is the novel structure of the information processing system.
Essentially a function approximation
Transforms inputs into outputs to the best of its ability
Types Neural NetworkArchitectures
Many kinds of structures, main distinction made between two classes:
a) Feed- forward (a directed acyclic graph (DAG): links are unidirectional, no cycles
- There is no internal state other than the weights.
b) Recurrent: links form arbitrary topologies e.g., Hopfield Networks and Boltzmann machines.
Recurrent networks: can be unstable, or oscillate, or exhibit chaotic behaviour e.g., given some input values, can take a long time to compute stable output and learning is made more difficult.
PRACTICAL IMPLEMENTATION
The usage of neural networks are as follows:
Classification
- Pattern recognition, feature extraction, image matching.
Noise Reduction
- Recognize patterns in the inputs and produce, noiseless outputs
Prediction
- Extrapolation based on historical data
THEORITICAL BASIS
WORKING OF NEURAL NETWORK:
- The output of a neuron is a function of the weighted sum of the inputs plus a bias.
The function of the entire neural network is simply the computation of the outputs of all the neurons
Where do the weights come from?
The weights in a neural network are the most important factor in determining its function.
Training is the act of presenting the network with some sample data and modifying the weights to better approximate the desired function.
There are two main types of training
Supervised Training
Supplies the neural network with inputs and the desired outputs.
Response of the network to the inputs is measured
The weights are modified to reduce the difference between the actual and desired outputs.
Unsupervised Training
Only supplies inputs
The neural network adjusts its own weights so that similar inputs cause similar outputs
IMPLEMENTATION USING THE MATLAB SOFTWARE
The Neural Network are good at fitting functions and recognizing patterns. Neural Networks are composed of simple elements operating in parallel.
Neural Networks are adjusted , or trained , so that a particular input leads to a specific target output.
Neural Networks have been trained to perform complex functions in various field s, including pattern recognition, identification, classification, speech, vision and control systems.
Neural Network can also be trained to solve the problems that are difficult for conventional computers or human beings.
The toolbox emphasize the use of neural network concept that build up in the use of financial , engineering and other practical applications.
Neural Network toolbox software for the control system describes three practical control system applications and other applications.