An Introduktion to Neural Networks

Автор(ы):Krose Ben, Patrick van der Smagt
06.10.2007
Год изд.:1996
Описание: This manuscript attempts to provide the reader with an insight in artificial neural networks. Back in 1990, the absence of any state-of-the-art textbook forced us into writing our own. However, in the meantime a number of worthwhile textbooks have been published which can be used for background and in-depth information. Some of the material in this book, especially parts III and IV, contains timely material and thus may heavily change throughout the ages. The choice of describing robotics and vision as neural network applications coincides with the neural network research interests of the authors. Much of the material presented in chapter 6 has been written by Joris van Dam and Anuj Dev at the University of Amsterdam. Also, Anuj contributed to material in chapter 9. The basis of chapter 7 was form by a report of Gerard Schram at the University of Amsterdam.
Оглавление:
An Introduktion to Neural Networks — обложка книги.
Preface [9]
I FUNDAMENTALS [11]
  1 Introduction [13]
  2 Fundamentals [15]
    2.1 A framework for distributed representation [15]
      2.1.1 Processing units [15]
      2.1.2 Connections between units [16]
      2.1.3 Activation and output rules [16]
    2.2 Network topologies [17]
    2.3 Training of artificial neural networks [18]
      2.3.1 Paradigms of learning [18]
      2.3.2 Modifying patterns of connectivity [18]
    2.4 Notation and terminology [18]
      2.4.1 Notation [19]
      2.4.2 Terminology [19]
II THEORY [21]
  3 Perceptron and Adaline [23]
    3.1 Networks with threshold activation functions [23]
    3.2 Perceptron learning rule and convergence theorem [24]
      3.2.1 Example of the Perceptron learning rule [25]
      3.2.2 Convergence theorem [25]
      3.2.3 The original Perceptron [26]
    3.3 The adaptive linear element (Adaline) [27]
    3.4 Networks with linear activation functions: the delta rule [28]
    3.5 Exclusive-OR problem [29]
    3.6 Multi-layer perceptrons can do everything [30]
    3.7 Conclusions [31]
  4 Back-Propagation [33]
    4.1 Multi-layer feed-forward networks [33]
    4.2 The generalised delta rule [33]
      4.2.1 Understanding back-propagation [35]
    4.3 Working with back-propagation [36]
    4.4 An example [37]
    4.5 Other activation functions [38]
    4.6 Deficiencies of back-propagation [39]
    4.7 Advanced algorithms [40]
    4.8 How good are multi-layer feed-forward networks? [42]
      4.8.1 The effect of the number of learning samples [43]
      4.8.2 The effect of the number of hidden units [44]
    4.9 Applications [45]
  5 Recurrent Networks [47]
    5.1 The generalised delta-rule in recurrent networks [47]
      5.1.1 The Jordan network [48]
      5.1.2 The Elman network [48]
      5.1.3 Back-propagation in fully recurrent networks [50]
    5.2 The Hopfield network [50]
      5.2.1 Description [50]
      5.2.2 Hopfield network as associative memory [52]
      5.2.3 Neurons with graded response [52]
    5.3 Boltzmann machines [54]
  6 Self-Organising Networks [57]
    6.1 Competitive learning [57]
      6.1.1 Clustering [57]
      6.1.2 Vector quantisation [61]
    6.2 Kohonen network [64]
    6.3 Principal component networks [66]
      6.3.1 Introduction [66]
      6.3.2 Normalised Hebbian rule [67]
      6.3.3 Principal component extractor [68]
      6.3.4 More eigenvectors [69]
    6.4 Adaptive resonance theory [69]
      6.4.1 Background: Adaptive resonance theory [69]
      6.4.2 ART1: The simplified neural network model [70]
      6.4.3 ART1: The original model [72]
  7 Reinforcement learning [75]
    7.1 The critic [75]
    7.2 The controller network [76]
    7.3 Barto's approach: the ASE-ACE combination [77]
      7.3.1 Associative search [77]
      7.3.2 Adaptive critic [78]
      7.3.3 The cart-pole system [79]
    7.4 Reinforcement learning versus optimal control [80]
III APPLICATIONS [83]
  8 Robot Control [85]
    8.1 End-effector positioning [86]
      8.1.1 Camera-robot coordination is function approximation [87]
    8.2 Robot arm dynamics [91]
    8.3 Mobile robots [94]
      8.3.1 Model based navigation [94]
      8.3.2 Sensor based control [95]
  9 Vision [97]
    9.1 Introduction [97]
    9.2 Feed-forward types of networks [97]
    9.3 Self-organising networks for image compression [98]
      9.3.1 Back-propagation [99]
      9.3.2 Linear networks [99]
      9.3.3 Principal components as features [99]
    9.4 The cognitron and neocognitron [100]
      9.4.1 Description of the cells [100]
      9.4.2 Structure of the cognitron [101]
      9.4.3 Simulation results [102]
    9.5 Relaxation types of networks [103]
      9.5.1 Depth from stereo [103]
      9.5.2 Image restoration and image segmentation [105]
      9.5.3 Silicon retina [105]
IV IMPLEMENTATIONS [107]
  10 General Purpose Hardware [111]
    10.1 The Connection Machine [112]
      10.1.1 Architecture [112]
      10.1.2 Applicability to neural networks [113]
    10.2 Systolic arrays [114]
  11 Dedicated Neuro-Hardware [115]
    11.1 General issues [115]
      11.1.1 Connectivity constraints [115]
      11.1.2 Analogue vs. digital [116]
      11.1.3 Optics [116]
      11.1.4 Learning vs. non-learning [117]
    11.2 Implementation examples [117]
      11.2.1 Carver Mead's silicon retina [117]
      11.2.2 LEP's LNeuro chip [119]
References [123]
Index [131]

List of Figures
  2.1 The basic components of an artificial neural network [16]
  2.2 Various activation functions for a unit [17]
  3.1 Single layer network with one output and two inputs [23]
  3.2 Geometric representation of the discriminant function and the weights [24]
  3.3 Discriminant function before and after weight update [25]
  3.4 The Perceptron [27]
  3.5 The Adaline [27]
  3.6 Geometric representation of input space [29]
  3.7 Solution of the XOR problem [30]
  4.1 A multi-layer network with (?) layers of units [34]
  4.2 The descent in weight space [37]
  4.3 Example of function approximation with a feedforward network [38]
  4.4 The periodic function f(x) = sin(2a;) sin(a;) approximated with sine activation functions [39]
  4.5 The periodic function f(x) = sin(2a;) sin(a;) approximated with sigmoid activation functions [40]
  4.6 Slow decrease with conjugate gradient in non-quadratic systems [42]
  4.7  Effect of the learning set size on the generalization [44]
  4.8 Effect of the learning set size on the error rate [44]
  4.9 Effect of the number of hidden units on the network performance [45]
  4.10 Effect of the number of hidden units on the error rate [45]
  5.1 The Jordan network [48]
  5.2 The Elman network [49]
  5.3 Training an Elman network to control an object [49]
  5.4 Training a feed-forward network to control an object [50]
  5.5 The auto-associator network [51]
  6.1 A simple competitive learning network [58]
  6.2 Example of clustering in 3D with normalised vectors [59]
  6.3 Determining the winner in a competitive learning network [59]
  6.4 Competitive learning for clustering data [61]
  6.5 Vector quantisation tracks input density [62]
  6.6 A network combining a vector quantisation layer with a 1-layer feed-forward neural network. This network can be used to approximate functions from -R2 to -R2, the input space -R2 is discretised in 5 disjoint subspaces [62]
  6.7 Gaussian neuron distance function [65]
  6.8 A topology-conserving map converging [65]
  6.9 The mapping of a two-dimensional input space on a one-dimensional Kohonen network [66]
  6.10 Mexican hat [66]
  6.11 Distribution of input samples [67]
  6.12 The ART architecture [70]
  6.13 The ART1 neural network [71]
  6.14  An example ART run [72]
  7.1 Reinforcement learning scheme [75]
  7.2 Architecture of a reinforcement learning scheme with critic element [78]
  7.3 The cart-pole system [80]
  8.1 An exemplar robot manipulator [85]
  8.2 Indirect learning system for robotics [88]
  8.3 The system used for specialised learning [89]
  8.4 A Kohonen network merging the output of two cameras [90]
  8.5 The neural model proposed by Kawato et al [92]
  8.6 The neural network used by Kawato et al [92]
  8.7 The desired joint pattern for joints 1. Joints 2 and 3 have similar time patterns [93]
  8.8 Schematic representation of the stored rooms, and the partial information which is available from a single sonar scan [95]
  8.9 The structure of the network for the autonomous land vehicle [95]
  9.1 Input image for the network [100]
  9.2 Weights of the PCA network [100]
  9.3 The basic structure of the cognitron [101]
  9.4 Cognitron receptive regions [102]
  9.5 Two learning iterations in the cognitron [103]
  9.6 Feeding back activation values in the cognitron [104]
  10.1 The Connection Machine system organisation [113]
  10.2 Typical use of a systolic array [114]
  10.3 The Warp system architecture [114]
  11.1 Connections between M input and N output neurons [115]
  11.2 Optical implementation of matrix multiplication [117]
  11.3 The photo-receptor used by Mead [ 118]
  11.4 The resistive layer (a) and, enlarged, a single node (b) [119]
  11.5 The LNeuro chip [120]
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