Principles of Artificial Neural Networks: Basic Designs to Deep Learning

Front Cover
World Scientific, Mar 15, 2019 - Computers - 440 pages

The field of Artificial Neural Networks is the fastest growing field in Information Technology and specifically, in Artificial Intelligence and Machine Learning.

This must-have compendium presents the theory and case studies of artificial neural networks. The volume, with 4 new chapters, updates the earlier edition by highlighting recent developments in Deep-Learning Neural Networks, which are the recent leading approaches to neural networks. Uniquely, the book also includes case studies of applications of neural networks — demonstrating how such case studies are designed, executed and how their results are obtained.

The title is written for a one-semester graduate or senior-level undergraduate course on artificial neural networks. It is also intended to be a self-study and a reference text for scientists, engineers and for researchers in medicine, finance and data mining.

 

Selected pages

Contents

Chapter 1 Introduction and Role of Artificial Neural Networks
1
Chapter 2 Fundamentals of Biological Neural Networks
5
Chapter 3 Basic Principles of ANNs and Their Structures
9
Chapter 4 The Perceptron
17
Chapter 5 The Madaline
37
Chapter 6 Back Propagation
59
Chapter 7 Hopfield Networks
123
Chapter 8 Counter Propagation
185
Chapter 12 Recurrent Time Cycling Back Propagation Networks
255
Principles and Scope
271
Chapter 14 Deep Learning Convolutional Neural Network
279
Chapter 15 LAMSTAR Neural Networks
293
Chapter 16 Performance of DLNN Comparative Case Studies
319
Problems
395
References
401
Author Index
415

Chapter 9 Adaptive Resonance Theory
203
Chapter 10 The Cognitron and Neocognitron
233
Chapter 11 Statistical Training
239
Subject Index
419
Copyright

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Principles of Artificial Neural Networks: Basic Designs to Deep Learning
Daniel Graupe
No preview available - 2019

Common terms and phrases

activation function algorithm applications ART-I artificial neural networks Back Propagation BigDecimal binary biological neural network cell Chap characters classification CNN network Cognitron computational speed conf[i considered convergence Convolution Layer counter cout database dataset sn][j datatrue sn deep learning neural denoting DLNN employed end end end endl energy function error expectedOutputs feature map for(int given Graupe Hence hidden layer Hopfield network initial input layer input vector input word Kohonen layer labelMap LAMSTAR network LAMSTAR-1 learning neural networks link weights LNN-1 and LNN-2 local minima Lyapunov stability Madaline Matlab module N_LAYERS Neocognitron noise output decision output layer parameters pattern Perceptron performance predict preprocessing problem processing random recognition RECOGNIZED TRAINING VECTOR sigmoid function simulated annealing speech recognition stochastic structure Study sub-word testing training data training set update void w_hidden w_hidden_min w_output winning neuron z_output_min θε

Bibliographic information

TitlePrinciples of Artificial Neural Networks: Basic Designs to Deep Learning
Volume 8 of Advanced Series in Circuits and Systems
AuthorDaniel Graupe
PublisherWorld Scientific, 2019
ISBN9811201242, 9789811201240
Length440 pages
Subjects ›  › 

Computers / Artificial Intelligence / General
Computers / Data Science / Neural Networks
  
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