07-02-2012, 04:10 PM
Adaptive Neuro-Fuzzy Inference System using Particle Swarm Optimization and it’s Application
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INTRODUCTION
The Adaptive Neuro-Fuzzy Inference System combines the concept of Fuzzy logic and Neural network to form a hybrid intelligent system that enhances the ability to automatically learn and adapt.
The Particle Swarm Optimization algorithm used to get the optimal values and parameters of our ANFIS model.
NEURAL NETWORK
A Neural network can be described as a system composed of many simple processing elements operating in parallel .
The function of NN is determined by network structure, connection strengths and the processing performed at computing elements or nodes.
It resembles the brain in two respects:
1. Knowledge is acquired by the network through a learning process.
2. Interneuron connection strengths known as synaptic weights are used to store the knowledge.
BACK PROPAGATION IN NEURAL NETWORK
Back Propagation learns by iteratively processing a set Of training data (samples). For each sample, weights are modified to minimize the error between network’s classification and actual classification.
The steps involved in back propagation are:
Initialize the weights and biases.
Feed the training sample.
Propagate the inputs forward; The net input has been computed and so as the output of each unit in the hidden and output layers.
Back propagate the error.
Update weights and biases to reflect the propagated errors.
Terminating conditions.
APPLICATION OF NEURAL NETWORK
Useful in the identification and control of dynamic
systems.
Optical character recognition.
Voice recognition.
Industrial process control.
Customer research.
Risk management.
