Principles of artificial neural networks basic designs to deep learning
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| Format: | Buch |
| Sprache: | English |
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Singapore ; Hackensack, NJ
World Scientific Publishing Co. Pte. Ltd.
[2019]
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| Ausgabe: | 4th edition |
| Schriftenreihe: | Advanced series in circuits and systems
8 |
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| Online-Zugang: | Inhaltsverzeichnis |
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| 245 | 1 | 0 | |a Principles of artificial neural networks |b basic designs to deep learning |c Daniel Graupe |
| 250 | |a 4th edition | ||
| 264 | 1 | |a Singapore ; Hackensack, NJ |b World Scientific Publishing Co. Pte. Ltd. |c [2019] | |
| 264 | 4 | |c © 2019 | |
| 300 | |a xvi, 422 Seiten |b Illustrationen, Diagramme |c 25 cm | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 1 | |a Advanced series in circuits and systems |v 8 | |
| 505 | 8 | |a Introduction and role of artificial neural networks -- Fundamentals of biological neural networks -- Basic principles of ANNs and their structures -- The perceptron -- The madaline -- Back propagation -- Hopfield networks -- Counter propagation -- Adaptive resonance theory -- The cognitron and neocognition -- Statistical training -- Recurrent (time cycling) back propagation networks -- Deep learning neural networks : principles and scope -- Deep learning convolutional neural network -- LAMSTAR neural networks -- Performance of DLNN : comparative case studies | |
| 650 | 0 | 7 | |a Neuronales Netz |0 (DE-588)4226127-2 |2 gnd |9 rswk-swf |
| 653 | 0 | |a Neural networks (Computer science) | |
| 653 | 0 | |a Neural networks (Computer science) | |
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| 830 | 0 | |a Advanced series in circuits and systems |v 8 |w (DE-604)BV016934728 |9 8 | |
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Datensatz im Suchindex
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| adam_text | Contents Acknowledgments vii Preface to the Fourth Edition ix Preface to the First Edition xi Chapter 1. Introduction and Role of Artificial NeuralNetworks 1 Chapter 2. Fundamentals of Biological Neural Networks 5 Chapter 3. Basic Principles of ANNs and Their Structures 3.1. 3.2. 3.3. 3.4. Chapter 4. Chapter 5. Chapter 6. Basic Principles of ANN Design.......................................... Basic Neural Structures...................................................... The Perceptron’s Input-Output Principles........................ The Adaline (ALC) ........................................................... 9 9 10 12 13 The Perceptron 17 4.1. The Basic Structure........................................................... 4.2. The Single-Layer Representation Problem........................ 4.3. The Limitations of the Single-Layer Perceptron............... 4.4. Many-Layer Perceptrons..................................................... 4.A. Perceptron Case Study: Identifying Autoregressive Parameters of a Signal (AR Time Series Identification) . . 17 22 22 24 25 The Madaline 37 5.1. Madaline Training.............................................................. 5.A. Madaline Case Study: Character Recognition................. 37 39 Back Propagation 59 6.1. 6.2. 59 59 The Back Propagation Learning Procedure..................... Derivation of the BP Algorithm.......................................... xiii
xiv Principles of Artificial and Neural Networks 6.3. Modified BP Algorithms..................................................... 63 6.A. Back Propagation Case Study: Character Recognition . . 65 6.B. Back Propagation Case Study: The Exclusive-OR (XOR) Problem (2-Layer BP)......................................................... 76 6.C. Back Propagation Case Study: The XOR Problem — 3 Layer BP Network............................................................ 94 6.D. Average Monthly High and Low Temperature Prediction Using Backpropagation Neural Networks..............................112 Chapter 7. Hopfield Networks 123 7.1. 7.2. 7.3. Introduction..............................................................................123 Binary Hopfield Networks......................................................123 Setting of Weights in Hopfield Nets — Bidirectional Associative Memory (BAM) Principle.................................125 7.4. Walsh Functions.....................................................................127 7.5. Network Stability.................................................................... 129 7.6. Summary of the Procedure for Implementing the Hopfield Network.....................................................................131 7.7. Continuous Hopfield Models...................................................132 7.8. The Continuous Energy (Lyapunov) Function..................... 133 7.A. Hopfield Network Case Study: Character Recognition . . 135 7.B. Hopfield Network Case Study: Traveling Salesman
Problem................................................................................... 147 7.C. Cell Shape Detection Using Neural Networks.................... 170 Chapter 8. Counter Propagation 8.1. 8.2. 8.3. 8.4. 8.5. 8.6. 8.A. Introduction..............................................................................185 Kohonen Self-Organizing Map (SOM) Layer........................186 Grossberg Layer .....................................................................186 Training of the Kohonen Layer.............................................187 Training of Grossberg Layers................................................189 The Combined Counter Propagation Network..................... 190 Counter Propagation Network Case Study: Character Recognition............................................................................. 190 Chapter 9. Adaptive Resonance Theory 9.1. 9.2. 9.3. 9.4. 9.5. 185 203 Motivation ............................................................................. 203 The ART Network Structure................................................203 Setting-Up of the ART Network ..........................................207 Network Operation..................................................................208 Properties of ART..................................................................209
Contents XV 9.6. Discussion and General Comments on ART-I and ART-II ................................................................................... 211 9.A. ART-I Network Case Study: Character Recognition ... 211 9.B. ART-I Case Study: Speech Recognition.............................225 Chapter 10. The Cognitron and Neocognitron 10.1. 10.2. 10.3. 10.4. 10.5. Introduction............................................................................. 233 The Basic Principles of the Cognitron.................................233 Network Operation................................................................. 234 Cognitron’s Network Training............................................... 235 The Neocognitron ................................................................. 237 Chapter 11. Statistical Training 11.1. 11.2. 11.3. 11.4. 11.5. 11.6. 11.A. 11.B. 255 Recurrent/Discrete Time Networks...................................... 255 Fully Recurrent Networks..................................................... 256 Continuously Recurrent Back Propagation Networks . . . 258 Recurrent Back Propagation Case Study: Character Recognition............................................................................. 258 Chapter 13. Deep Learning Neural Networks: Principles and Scope 13.1. 13.2. 13.3. 13.4. 239 Fundamental Philosophy........................................................ 239 Annealing Methods................................................................. 240 Simulated Annealing by Boltzmann Training of Weights . 240 Stochastic Determination of Magnitude of Weight
Change................................................................................... 241 Temperature-Equivalent Setting...................................... . 241 Cauchy Training of Neural Network ....................................242 Statistical Training Case Study: A Stochastic Hopfield Network for Character Recognition...................................... 243 Statistical Training Case Study: Identifying AR Signal Parameters with a Stochastic Perceptron Model..................246 Chapter 12. Recurrent (Time Cycling) Back Propagation Networks 12.1. 12.2. 12.3. 12.A. 233 271 Definition................................................................................ 271 Brief History of DNN and of Its Applications.....................272 The Scope of DLNN.............................................................. 274 Introduction to Specific DLNN Algorithms ........................274 Chapter 14. Deep Learning Convolutional Neural Network 279 14.1. Introduction............................................................................. 279 14.2. Feed-Forward Loop................................................................. 280 14.3. The Convolution Layer........................................................... 283
xvi Principles of Artificial and Neural Networks 14.4. Back Propagation .................................................................... 286 14.5. ReLu Layers ............................................................................. 286 14.6. Pooling Layers.......................................................................... 287 14.7. Dropout.......................................................................................288 14.8. Output FC Layer....................................................................... 289 14.9. Parameter (Weight) Sharing.....................................................289 14.10. Applications................................................................................ 290 Chapter 15. LAMSTAR Neural Networks 15.1. 15.2. 15.3. 15.4. 15.5. 293 LAMSTAR Principles..............................................................293 LAMSTAR-1 (LNN-1)..............................................................305 LAMSTAR-2 (LNN-2)..............................................................305 Data Analysis LAMSTAR........................................................311 Comments and Applications.....................................................315 Chapter 16. Performance of DLNN— Comparative Case Studies 319 16.1. Case Studies ............................................................................. 319 16.2. Comparative Tabulation of Performance and Computational Speed ..............................................................344 Problems 395 References 401 Author Index 415 Subject Index 419
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| any_adam_object | 1 |
| author | Graupe, Daniel |
| author_GND | (DE-588)1129263053 |
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| author_sort | Graupe, Daniel |
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| classification_rvk | ST 301 |
| contents | Introduction and role of artificial neural networks -- Fundamentals of biological neural networks -- Basic principles of ANNs and their structures -- The perceptron -- The madaline -- Back propagation -- Hopfield networks -- Counter propagation -- Adaptive resonance theory -- The cognitron and neocognition -- Statistical training -- Recurrent (time cycling) back propagation networks -- Deep learning neural networks : principles and scope -- Deep learning convolutional neural network -- LAMSTAR neural networks -- Performance of DLNN : comparative case studies |
| ctrlnum | (OCoLC)1124793440 (DE-599)BVBBV046093970 |
| discipline | Informatik |
| edition | 4th edition |
| format | Book |
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| id | DE-604.BV046093970 |
| illustrated | Illustrated |
| indexdate | 2024-12-24T07:46:27Z |
| institution | BVB |
| isbn | 9789811201226 9811201226 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-031474884 |
| oclc_num | 1124793440 |
| open_access_boolean | |
| owner | DE-739 DE-83 DE-898 DE-BY-UBR |
| owner_facet | DE-739 DE-83 DE-898 DE-BY-UBR |
| physical | xvi, 422 Seiten Illustrationen, Diagramme 25 cm |
| publishDate | 2019 |
| publishDateSearch | 2019 |
| publishDateSort | 2019 |
| publisher | World Scientific Publishing Co. Pte. Ltd. |
| record_format | marc |
| series | Advanced series in circuits and systems |
| series2 | Advanced series in circuits and systems |
| spellingShingle | Graupe, Daniel Principles of artificial neural networks basic designs to deep learning Advanced series in circuits and systems Introduction and role of artificial neural networks -- Fundamentals of biological neural networks -- Basic principles of ANNs and their structures -- The perceptron -- The madaline -- Back propagation -- Hopfield networks -- Counter propagation -- Adaptive resonance theory -- The cognitron and neocognition -- Statistical training -- Recurrent (time cycling) back propagation networks -- Deep learning neural networks : principles and scope -- Deep learning convolutional neural network -- LAMSTAR neural networks -- Performance of DLNN : comparative case studies Neuronales Netz (DE-588)4226127-2 gnd |
| subject_GND | (DE-588)4226127-2 |
| title | Principles of artificial neural networks basic designs to deep learning |
| title_auth | Principles of artificial neural networks basic designs to deep learning |
| title_exact_search | Principles of artificial neural networks basic designs to deep learning |
| title_full | Principles of artificial neural networks basic designs to deep learning Daniel Graupe |
| title_fullStr | Principles of artificial neural networks basic designs to deep learning Daniel Graupe |
| title_full_unstemmed | Principles of artificial neural networks basic designs to deep learning Daniel Graupe |
| title_short | Principles of artificial neural networks |
| title_sort | principles of artificial neural networks basic designs to deep learning |
| title_sub | basic designs to deep learning |
| topic | Neuronales Netz (DE-588)4226127-2 gnd |
| topic_facet | Neuronales Netz |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=031474884&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV016934728 |
| work_keys_str_mv | AT graupedaniel principlesofartificialneuralnetworksbasicdesignstodeeplearning |