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Principles of artificial neural networks [[electronic resource] /] / Daniel Graupe
Principles of artificial neural networks [[electronic resource] /] / Daniel Graupe
Autore Graupe Daniel
Edizione [3rd ed.]
Pubbl/distr/stampa Singapore ; ; Hackensack, N.J., : World Scientific, 2013
Descrizione fisica 1 online resource (382 pages)
Disciplina 006.32
Collana Advanced series in circuits and systems
Soggetto topico Neural networks (Computer science)
Soggetto genere / forma Electronic books.
ISBN 981-4522-74-0
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Acknowledgments; Preface to the Third Edition; Preface to the Second Edition; Preface to the First Edition; Contents; Chapter 1. Introduction and Role of Artificial Neural Networks; Chapter 2. Fundamentals of Biological Neural Networks; Chapter 3. Basic Principles of ANNs and Their Early Structures; 3.1. Basic Principles of ANN Design; 3.2. Basic Network Structures; 3.3. The Perceptron's Input-Output Principles; 3.4. The Adaline (ALC); 3.4.1. LMS training of ALC; 3.4.2. Steepest descent training of ALC; Chapter 4. The Perceptron; 4.1. The Basic Structure
4.1.1. Perceptron's activation functions4.2. 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); Chapter 5. The Madaline; 5.1. Madaline Training; 5.A. Madaline Case Study: Character Recognition; 5.A.1. Problem statement; 5.A.2. Design of network; 5.A.3. Training of the network; 5.A.4. Results; 5.A.5. Conclusions and observations; 5.A.6. MATLAB source code for implementing MADALINE network
Chapter 6. Back Propagation6.1. The Back Propagation Learning Procedure; 6.2. Derivation of the BP Algorithm; 6.3. Modified BP Algorithms; 6.3.1. Introduction of bias into NN; 6.3.2. Incorporating momentum or smoothing to weight adjustment; 6.3.3. Other modification concerning convergence; 6.A. Back Propagation Case Study: Character Recognition; 6.A.1. Introduction; 6.A.2. Network design; 6.A.3. Results; 6.A.4. Discussion and conclusions; 6.A.5. Source Code (C++); 6.B. Back Propagation Case Study: The Exclusive-OR (XOR) Problem (2-Layer BP)
6.C. Back Propagation Case Study: The XOR Problem - 3 Layer BP Network6.D. Average Monthly High and Low Temperature Prediction Using Backpropagation Neural Networks; 6.D.1. Introduction; 6.D.2. Design; 6.D.3. Results; 6.D.4. Conclusion; 6.D.5. Source Code (Matlab); Chapter 7. Hopfield Networks; 7.1. Introduction; 7.2. Binary Hopfield Networks; 7.3. Setting of Weights in Hopfield Nets - Bidirectional Associative Memory (BAM) Principle; 7.4. Walsh Functions; 7.5. Network Stability; 7.6. Summary of the Procedure for Implementing the Hopfield Network; 7.7. Continuous Hopfield Models
7.8. The Continuous Energy (Lyapunov) Function7.A. Hopfield Network Case Study: Character Recognition; 7.A.1. Introduction; 7.A.2. Network design; 7.A.3. Setting of weights; 7.A.4. Testing; 7.A.5. Results and conclusions; 7.A.6. MATALAB source codes; 7.B. Hopfield Network Case Study: Traveling Salesman Problem; 7.B.1. Introduction; 7.B.2. Hopfield neural network design; 7.B.2.1. The energy function; 7.B.2.2. Weight matrix setting; 7.B.2.3. Activation function; 7.B.2.4. The activation function; 7.B.3. Input selection; 7.B.4. Implementation details; 7.B.5. Output results
7.B.6. Concluding discussion
Record Nr. UNINA-9910452271503321
Graupe Daniel  
Singapore ; ; Hackensack, N.J., : World Scientific, 2013
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Principles of artificial neural networks / / Daniel Graupe, University of Illinois, Chicago, USA
Principles of artificial neural networks / / Daniel Graupe, University of Illinois, Chicago, USA
Autore Graupe Daniel
Edizione [3rd ed.]
Pubbl/distr/stampa Singapore ; ; Hackensack, N.J., : World Scientific, 2013
Descrizione fisica 1 online resource (xviii, 363 pages) : illustrations (some color)
Disciplina 006.32
Collana Advanced series in circuits and systems
Soggetto topico Neural networks (Computer science)
ISBN 981-4522-74-0
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Acknowledgments; Preface to the Third Edition; Preface to the Second Edition; Preface to the First Edition; Contents; Chapter 1. Introduction and Role of Artificial Neural Networks; Chapter 2. Fundamentals of Biological Neural Networks; Chapter 3. Basic Principles of ANNs and Their Early Structures; 3.1. Basic Principles of ANN Design; 3.2. Basic Network Structures; 3.3. The Perceptron's Input-Output Principles; 3.4. The Adaline (ALC); 3.4.1. LMS training of ALC; 3.4.2. Steepest descent training of ALC; Chapter 4. The Perceptron; 4.1. The Basic Structure
4.1.1. Perceptron's activation functions4.2. 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); Chapter 5. The Madaline; 5.1. Madaline Training; 5.A. Madaline Case Study: Character Recognition; 5.A.1. Problem statement; 5.A.2. Design of network; 5.A.3. Training of the network; 5.A.4. Results; 5.A.5. Conclusions and observations; 5.A.6. MATLAB source code for implementing MADALINE network
Chapter 6. Back Propagation6.1. The Back Propagation Learning Procedure; 6.2. Derivation of the BP Algorithm; 6.3. Modified BP Algorithms; 6.3.1. Introduction of bias into NN; 6.3.2. Incorporating momentum or smoothing to weight adjustment; 6.3.3. Other modification concerning convergence; 6.A. Back Propagation Case Study: Character Recognition; 6.A.1. Introduction; 6.A.2. Network design; 6.A.3. Results; 6.A.4. Discussion and conclusions; 6.A.5. Source Code (C++); 6.B. Back Propagation Case Study: The Exclusive-OR (XOR) Problem (2-Layer BP)
6.C. Back Propagation Case Study: The XOR Problem - 3 Layer BP Network6.D. Average Monthly High and Low Temperature Prediction Using Backpropagation Neural Networks; 6.D.1. Introduction; 6.D.2. Design; 6.D.3. Results; 6.D.4. Conclusion; 6.D.5. Source Code (Matlab); Chapter 7. Hopfield Networks; 7.1. Introduction; 7.2. Binary Hopfield Networks; 7.3. Setting of Weights in Hopfield Nets - Bidirectional Associative Memory (BAM) Principle; 7.4. Walsh Functions; 7.5. Network Stability; 7.6. Summary of the Procedure for Implementing the Hopfield Network; 7.7. Continuous Hopfield Models
7.8. The Continuous Energy (Lyapunov) Function7.A. Hopfield Network Case Study: Character Recognition; 7.A.1. Introduction; 7.A.2. Network design; 7.A.3. Setting of weights; 7.A.4. Testing; 7.A.5. Results and conclusions; 7.A.6. MATALAB source codes; 7.B. Hopfield Network Case Study: Traveling Salesman Problem; 7.B.1. Introduction; 7.B.2. Hopfield neural network design; 7.B.2.1. The energy function; 7.B.2.2. Weight matrix setting; 7.B.2.3. Activation function; 7.B.2.4. The activation function; 7.B.3. Input selection; 7.B.4. Implementation details; 7.B.5. Output results
7.B.6. Concluding discussion
Record Nr. UNINA-9910779986703321
Graupe Daniel  
Singapore ; ; Hackensack, N.J., : World Scientific, 2013
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Principles of artificial neural networks / / Daniel Graupe, University of Illinois, Chicago, USA
Principles of artificial neural networks / / Daniel Graupe, University of Illinois, Chicago, USA
Autore Graupe Daniel
Edizione [3rd ed.]
Pubbl/distr/stampa Singapore ; ; Hackensack, N.J., : World Scientific, 2013
Descrizione fisica 1 online resource (xviii, 363 pages) : illustrations (some color)
Disciplina 006.32
Collana Advanced series in circuits and systems
Soggetto topico Neural networks (Computer science)
ISBN 981-4522-74-0
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Acknowledgments; Preface to the Third Edition; Preface to the Second Edition; Preface to the First Edition; Contents; Chapter 1. Introduction and Role of Artificial Neural Networks; Chapter 2. Fundamentals of Biological Neural Networks; Chapter 3. Basic Principles of ANNs and Their Early Structures; 3.1. Basic Principles of ANN Design; 3.2. Basic Network Structures; 3.3. The Perceptron's Input-Output Principles; 3.4. The Adaline (ALC); 3.4.1. LMS training of ALC; 3.4.2. Steepest descent training of ALC; Chapter 4. The Perceptron; 4.1. The Basic Structure
4.1.1. Perceptron's activation functions4.2. 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); Chapter 5. The Madaline; 5.1. Madaline Training; 5.A. Madaline Case Study: Character Recognition; 5.A.1. Problem statement; 5.A.2. Design of network; 5.A.3. Training of the network; 5.A.4. Results; 5.A.5. Conclusions and observations; 5.A.6. MATLAB source code for implementing MADALINE network
Chapter 6. Back Propagation6.1. The Back Propagation Learning Procedure; 6.2. Derivation of the BP Algorithm; 6.3. Modified BP Algorithms; 6.3.1. Introduction of bias into NN; 6.3.2. Incorporating momentum or smoothing to weight adjustment; 6.3.3. Other modification concerning convergence; 6.A. Back Propagation Case Study: Character Recognition; 6.A.1. Introduction; 6.A.2. Network design; 6.A.3. Results; 6.A.4. Discussion and conclusions; 6.A.5. Source Code (C++); 6.B. Back Propagation Case Study: The Exclusive-OR (XOR) Problem (2-Layer BP)
6.C. Back Propagation Case Study: The XOR Problem - 3 Layer BP Network6.D. Average Monthly High and Low Temperature Prediction Using Backpropagation Neural Networks; 6.D.1. Introduction; 6.D.2. Design; 6.D.3. Results; 6.D.4. Conclusion; 6.D.5. Source Code (Matlab); Chapter 7. Hopfield Networks; 7.1. Introduction; 7.2. Binary Hopfield Networks; 7.3. Setting of Weights in Hopfield Nets - Bidirectional Associative Memory (BAM) Principle; 7.4. Walsh Functions; 7.5. Network Stability; 7.6. Summary of the Procedure for Implementing the Hopfield Network; 7.7. Continuous Hopfield Models
7.8. The Continuous Energy (Lyapunov) Function7.A. Hopfield Network Case Study: Character Recognition; 7.A.1. Introduction; 7.A.2. Network design; 7.A.3. Setting of weights; 7.A.4. Testing; 7.A.5. Results and conclusions; 7.A.6. MATALAB source codes; 7.B. Hopfield Network Case Study: Traveling Salesman Problem; 7.B.1. Introduction; 7.B.2. Hopfield neural network design; 7.B.2.1. The energy function; 7.B.2.2. Weight matrix setting; 7.B.2.3. Activation function; 7.B.2.4. The activation function; 7.B.3. Input selection; 7.B.4. Implementation details; 7.B.5. Output results
7.B.6. Concluding discussion
Record Nr. UNINA-9910821027103321
Graupe Daniel  
Singapore ; ; Hackensack, N.J., : World Scientific, 2013
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Principles of artificial neural networks [[electronic resource] /] / Daniel Graupe
Principles of artificial neural networks [[electronic resource] /] / Daniel Graupe
Autore Graupe Daniel
Edizione [2nd ed.]
Pubbl/distr/stampa Singapore ; ; Hackensack, N.J., : World Scientific, c2007
Descrizione fisica 1 online resource (320 p.)
Disciplina 006.3/2
Collana Advanced series on circuits and systems
Soggetto topico Neural networks (Computer science)
Soggetto genere / forma Electronic books.
ISBN 1-281-12170-3
9786611121709
981-277-057-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Acknowledgments; Preface to the First Edition; Preface to the Second Edition; Contents; Chapter 1. Introduction and Role of Artificial Neural Networks; Chapter 2. Fundamentals of Biological Neural Networks; Chapter 3. Basic Principles of ANNs and Their Early Structures; Chapter 4. The Perceptron; Chapter 5. The Madaline; Chapter 6. Back Propagation; Chapter 7. Hopeld Networks; Chapter 8. Counter Propagation; Chapter 9. Adaptive Resonance Theory; Chapter 10. The Cognitron and the Neocognitron; Chapter 11. Statistical Training; Chapter 12. Recurrent (Time Cycling) Back Propagation Networks
Chapter 13. Large Scale Memory Storage and Retrieval (LAMSTAR) Network Problems; References; Author Index; Subject Index
Record Nr. UNINA-9910450698103321
Graupe Daniel  
Singapore ; ; Hackensack, N.J., : World Scientific, c2007
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Principles of artificial neural networks [[electronic resource] /] / Daniel Graupe
Principles of artificial neural networks [[electronic resource] /] / Daniel Graupe
Autore Graupe Daniel
Edizione [2nd ed.]
Pubbl/distr/stampa Singapore ; ; Hackensack, N.J., : World Scientific, c2007
Descrizione fisica 1 online resource (320 p.)
Disciplina 006.3/2
Collana Advanced series on circuits and systems
Soggetto topico Neural networks (Computer science)
ISBN 1-281-12170-3
9786611121709
981-277-057-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Acknowledgments; Preface to the First Edition; Preface to the Second Edition; Contents; Chapter 1. Introduction and Role of Artificial Neural Networks; Chapter 2. Fundamentals of Biological Neural Networks; Chapter 3. Basic Principles of ANNs and Their Early Structures; Chapter 4. The Perceptron; Chapter 5. The Madaline; Chapter 6. Back Propagation; Chapter 7. Hopeld Networks; Chapter 8. Counter Propagation; Chapter 9. Adaptive Resonance Theory; Chapter 10. The Cognitron and the Neocognitron; Chapter 11. Statistical Training; Chapter 12. Recurrent (Time Cycling) Back Propagation Networks
Chapter 13. Large Scale Memory Storage and Retrieval (LAMSTAR) Network Problems; References; Author Index; Subject Index
Record Nr. UNINA-9910784070603321
Graupe Daniel  
Singapore ; ; Hackensack, N.J., : World Scientific, c2007
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Principles of artificial neural networks / / Daniel Graupe
Principles of artificial neural networks / / Daniel Graupe
Autore Graupe Daniel
Edizione [2nd ed.]
Pubbl/distr/stampa Singapore ; ; Hackensack, N.J., : World Scientific, c2007
Descrizione fisica 1 online resource (320 p.)
Disciplina 006.3/2
Collana Advanced series on circuits and systems
Soggetto topico Neural networks (Computer science)
ISBN 1-281-12170-3
9786611121709
981-277-057-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Acknowledgments; Preface to the First Edition; Preface to the Second Edition; Contents; Chapter 1. Introduction and Role of Artificial Neural Networks; Chapter 2. Fundamentals of Biological Neural Networks; Chapter 3. Basic Principles of ANNs and Their Early Structures; Chapter 4. The Perceptron; Chapter 5. The Madaline; Chapter 6. Back Propagation; Chapter 7. Hopeld Networks; Chapter 8. Counter Propagation; Chapter 9. Adaptive Resonance Theory; Chapter 10. The Cognitron and the Neocognitron; Chapter 11. Statistical Training; Chapter 12. Recurrent (Time Cycling) Back Propagation Networks
Chapter 13. Large Scale Memory Storage and Retrieval (LAMSTAR) Network Problems; References; Author Index; Subject Index
Record Nr. UNINA-9910815601003321
Graupe Daniel  
Singapore ; ; Hackensack, N.J., : World Scientific, c2007
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui