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Neural Networks An Introduction.

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Presentation on theme: "Neural Networks An Introduction."— Presentation transcript:

1 Neural Networks An Introduction

2 An Introduction to Neural Networks
A Neuron An Introduction to Neural Networks

3 Computer Representation
Output= a(n) = a(pw+b) An Introduction to Neural Networks

4 A Single Neuron with Multiple Inputs
An Introduction to Neural Networks

5 Single Layer Neural Network with Multiple Neurons
An Introduction to Neural Networks

6 Multiple Layer Neural Network
An Introduction to Neural Networks

7 An Introduction to Neural Networks
Activation Functions Hard Limit a = 0 n < 0 a = 1 n >= 0 Symmetrical Hard Limit a = -1 n < 0 a = +1 n >= 0 Saturating Linear a = n 0 <= n <= 1 a = 1 n > 1 An Introduction to Neural Networks

8 An Introduction to Neural Networks
Activation Functions Linear a = n Symmetric Saturating Linear a = -1 n < -1 a = n -1 <= n <= 1 a = 1 n > 1 Log-Sigmoid a = 1 1+ e-n An Introduction to Neural Networks

9 An Introduction to Neural Networks
Activation Functions Hyperbolic Tangent Sigmoid a = en - e-n en + e-n Positive Linear a = 0 n < 0 a = n n >= 0 Competitive a = 1 neuron with max n a = 0 all other neurons An Introduction to Neural Networks

10 The History of Development of Neural Networks
The Beginning of Neural Networks (1940's) McCulloch Pitts Neuron Hebb Learning The First Golden Age of Neural Networks (1950's and 1960's) Perceptrons Adaline The Quiet Years: 1970's Kohonen Anderson Grossberg Carpenter Renewed Enthusiasm: 1980's Backpropagation Hopfield nets Neocognitron Boltzman machine Hardware Implementation An Introduction to Neural Networks

11 Developing a Neural Network System
Choose a neural network architecture Train the neural network using a training set Apply the neural network to identify patterns. This involves implementing the application algorithm An Introduction to Neural Networks

12 Choosing a Neural Network Architecture
Identify the number of inputs Number of network inputs = number of problem inputs. Identify the number of outputs Number of neurons in output layer = number of problem outputs. The output layer transfer function is partly determined by problem specification of the outputs. An Introduction to Neural Networks


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