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Artificial Neural Networks (Advantages (Adaptive learning, Self…
Artificial Neural Networks
Introduction
Biologically inspired simulations performed on the computer to perform certain specific tasks like clustering, classification, pattern recognition etc.
Learning Techniques
Supervised Learning
Unsupervised Learning
Backpropagation Learning
Hebbian Learning
Perception Learning
History
The first artificial neuron was produced in 1943 by neuro physiologist Warren McCulloch and logic Walter Pits
In 1951, Marvin Minsky created the first ANN while working at Princeton.
The Mark I Perceptron was also created in 1958, at Cornell University by Frank Rosenblatt
Uses
Classification
A Neural Network can be trained to classify given pattern or data set into predefined class. It uses Feedforward Networks.
Prediction
A Neural Network can be trained to produce outputs that are expected from given input. E.g., - Stock market prediction.
Clustering
The Neural network can be used to identify a unique feature of the data and classify them into different categories without any prior knowledge of the data.
Association
A Neural Network can be trained to remember the particular pattern, so that when the noise pattern is presented to the network..
Advantages
Adaptive learning
Self-Organization
Real Time
Operation
Fault Tolerance
Redundant
Information Coding
Components
Connections and weights
Propagation function
Neurons
Learning rule
Architecture
Input layer
Contains Artificial Neurons which receive input from the outside world on which network will learn, recognize about or otherwise process.
Outpuy layer
It contains units that respond to the information about how it's learned any task.
Hidden layer
These units are in between input and output layers. The job of hidden layer is to transform the input into something that output unit can use in some way.
Training Algorithms
Gradient Descent Algorithm
Back Propagation Algorithm
Hebb Rule
Self - Organizing Kohonen Rule
Hopfield law
LMS algorithm (Least Mean Square)
Competitive Learning