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Krose B.N. An introduction to neural network

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Автор: Krose B.N.
Название: An introduction to neural network
Год издания: 1996
УДК: 681.322
Число страниц: 135
Содержание книги:
2.1 A framework for distributed representation
2.1.1 Processing units
2.1.2 Connections between units
2.1.3 Activation and output rules
2.2 Network topologies
2.3 Training of artificial neural networks
2.3.1 Paradigms of learning
2.3.2 Modifying patterns of connectivity
2.4 Notation and terminology
2.4.1 Notation
2.4.2 Terminology
3.1 Networks with threshold activation functions
3.2 Perceptron learning rule and convergence theorem
3.2.1 Example of the Perceptron learning rule
3.2.2 Convergence theorem
3.2.3 The original Perceptron
3.3 The adaptive linear element (Adaline
3.4 Networks with linear activation functions: the delta rule
3.5 Exclusive-OR problem
3.6 Multi-layer perceptrons can do everything
3.7 Conclusions
4.1 Multi-layer feed-forward networks
4.2 The generalised delta rule
4.2.1 Understanding back-propagation
4.3 Working with back-propagation
4.4 An example
4.5 Other activation functions
4.6 Deficiencies of back-propagation
4.7 Advanced algorithms
4.8 How good are multi-layer feed-forward networks
4.8.1 The effect of the number of learning samples
4.8.2 The effect of the number of hidden units
4.9 Applications
5.1 The generalised delta-rule in recurrent networks
5.1.1 The Jordan network
5.1.2 The Elman network
5.1.3 Back-propagation in fully recurrent networks
5.2 The Hopfield network
5.2.1 Description
5.2.2 Hopfield network as associative memory
5.2.3 Neurons with graded response
5.3 Boltzmann machines
6.1 Competitive learning
6.1.1 Clustering
6.1.2 Vector quantisation
6.2 Kohonen network
6.3 Principal component networks
6.3.1 Introduction
6.3.2 Normalised Hebbian rule
6.3.3 Principal component extractor
6.3.4 More eigenvectors
6.4 Adaptive resonance theory
6.4.1 Background: Adaptive resonance theory
6.4.2 ART1: The simplified neural network model
6.4.3 ART1: The original model
7.1 The critic
7.2 The controller network
7.3 Barto's approach: the ASE-ACE combination
7.3.1 Associative search
7.3.2 Adaptive critic
7.3.3 The cart-pole system
7.4 Reinforcement learning versus optimal control
8.1 End-effector positioning
8.1.1 Camera-robot coordination is function approximation
8.2 Robot arm dynamics
8.3 Mobile robots
8.3.1 Model based navigation
8.3.2 Sensor based control
9.1 Introduction
9.2 Feed-forward types of networks
9.3 Self-organising networks for image compression
9.3.1 Back-propagation
9.3.2 Linear networks
9.3.3 Principal components as features
9.4 The cognitron and neocognitron
9.4.1 Description of the cells
9.4.2 Structure of the cognitron
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Главный редактор проекта: Мавлютов Р.Р.
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