Info
- Utilizzare la checkbox di selezione a fianco di ciascun documento per attivare le funzionalità di stampa, invio email, download nei formati disponibili del (i) record.
Proceedings of 2005 International Conference on Neural Networks and Brain : Oct. 13-15, 2005, Beijing, China
| Proceedings of 2005 International Conference on Neural Networks and Brain : Oct. 13-15, 2005, Beijing, China |
| Pubbl/distr/stampa | [Place of publication not identified], : IEEE, 2005 |
| Disciplina | 006.3/2 |
| Soggetto topico |
Neural networks (Computer science)
Artificial intelligence Engineering & Applied Sciences Computer Science |
| ISBN | 1-5386-0222-9 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996207345003316 |
| [Place of publication not identified], : IEEE, 2005 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Proceedings of 2005 International Conference on Neural Networks and Brain : Oct. 13-15, 2005, Beijing, China
| Proceedings of 2005 International Conference on Neural Networks and Brain : Oct. 13-15, 2005, Beijing, China |
| Pubbl/distr/stampa | [Place of publication not identified], : IEEE, 2005 |
| Disciplina | 006.3/2 |
| Soggetto topico |
Neural networks (Computer science)
Artificial intelligence Engineering & Applied Sciences Computer Science |
| ISBN | 1-5386-0222-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910145617303321 |
| [Place of publication not identified], : IEEE, 2005 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Proceedings of the International Joint Conference on Neural Networks (IJCNN) 2005 : July 31 - August 4, 2005, Hilton Montréal Bonaventure Hotel, Montréal, Québec, Canada
| Proceedings of the International Joint Conference on Neural Networks (IJCNN) 2005 : July 31 - August 4, 2005, Hilton Montréal Bonaventure Hotel, Montréal, Québec, Canada |
| Pubbl/distr/stampa | [Place of publication not identified], : IEEE Operations Center, 2005 |
| Disciplina | 006.3/2 |
| Soggetto topico |
Neural networks (Computer science)
Engineering & Applied Sciences Computer Science |
| ISBN | 1-5090-9704-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996199730103316 |
| [Place of publication not identified], : IEEE Operations Center, 2005 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Proceedings of the International Joint Conference on Neural Networks (IJCNN) 2005 : July 31 - August 4, 2005, Hilton Montréal Bonaventure Hotel, Montréal, Québec, Canada
| Proceedings of the International Joint Conference on Neural Networks (IJCNN) 2005 : July 31 - August 4, 2005, Hilton Montréal Bonaventure Hotel, Montréal, Québec, Canada |
| Pubbl/distr/stampa | [Place of publication not identified], : IEEE Operations Center, 2005 |
| Disciplina | 006.3/2 |
| Soggetto topico |
Neural networks (Computer science)
Engineering & Applied Sciences Computer Science |
| ISBN | 1-5090-9704-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910142339803321 |
| [Place of publication not identified], : IEEE Operations Center, 2005 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
The second IEEE International Conference on Cognitive Informatics : proceedings : 18-20 August 2003, London, England
| The second IEEE International Conference on Cognitive Informatics : proceedings : 18-20 August 2003, London, England |
| Pubbl/distr/stampa | [Place of publication not identified], : IEEE Computer Society, 2003 |
| Disciplina | 006.3/2 |
| Soggetto topico |
Neural computers
Cognitive science Artificial intelligence Computer Science Engineering & Applied Sciences |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996210893903316 |
| [Place of publication not identified], : IEEE Computer Society, 2003 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Static and dynamic neural networks : from fundamentals to advanced theory / / Madan M. Gupta, Liang Jin, Noriyasu Homma
| Static and dynamic neural networks : from fundamentals to advanced theory / / Madan M. Gupta, Liang Jin, Noriyasu Homma |
| Autore | Gupta Madan M. |
| Pubbl/distr/stampa | [Hoboken, New Jersey] : , : Wiley, , 2003 |
| Descrizione fisica | 1 online resource (751 p.) |
| Disciplina |
006.3/2
006.32 |
| Altri autori (Persone) |
JinLiang
HommaNoriyasu |
| Soggetto topico | Neural networks (Computer science) |
| Soggetto non controllato | Electrical and Electronics Engineering |
| ISBN |
1-280-54179-2
9786610541799 0-470-30378-6 0-471-46092-3 0-471-42795-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Foreword: Lotfi A. Zadeh. -- Preface. -- Acknowledgments. -- PART I: FOUNDATIONS OF NEURAL NETWORKS. -- Neural Systems: An Introduction. -- Biological Foundations of Neuronal Morphology. -- Neural Units: Concepts, Models, and Learning. -- PART II: STATIC NEURAL NETWORKS. -- Multilayered Feedforward Neural Networks (MFNNs) and Backpropagation Learning Algorithms. -- Advanced Methods for Learning Adaptation in MFNNs. -- Radial Basis Function Neural Networks. -- Function Approximation Using Feedforward Neural Networks. -- PART III: DYNAMIC NEURAL NETWORKS. -- Dynamic Neural Units (DNUs): Nonlinear Models and Dynamics. -- Continuous-Time Dynamic Neural Networks. -- Learning and Adaptation in Dynamic Neural Networks. -- Stability of Continuous-Time Dynamic Neural Networks. -- Discrete-Time Dynamic Neural Networks and Their Stability. -- PART IV: SOME ADVANCED TOPICS IN NEURAL NETWORKS. -- Binary Neural Networks. -- Feedback Binary Associative Memories. -- Fuzzy Sets and Fuzzy Neural Networks. -- References and Bibliography. -- Appendix A: Current Bibliographic Sources on Neural Networks. -- Index. |
| Record Nr. | UNINA-9910143225303321 |
Gupta Madan M.
|
||
| [Hoboken, New Jersey] : , : Wiley, , 2003 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Static and dynamic neural networks : from fundamentals to advanced theory / / Madan M. Gupta, Liang Jin, Noriyasu Homma
| Static and dynamic neural networks : from fundamentals to advanced theory / / Madan M. Gupta, Liang Jin, Noriyasu Homma |
| Autore | Gupta Madan M. |
| Pubbl/distr/stampa | [Hoboken, New Jersey] : , : Wiley, , 2003 |
| Descrizione fisica | 1 online resource (751 p.) |
| Disciplina |
006.3/2
006.32 |
| Altri autori (Persone) |
JinLiang
HommaNoriyasu |
| Soggetto topico | Neural networks (Computer science) |
| Soggetto non controllato | Electrical and Electronics Engineering |
| ISBN |
1-280-54179-2
9786610541799 0-470-30378-6 0-471-46092-3 0-471-42795-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Foreword: Lotfi A. Zadeh. -- Preface. -- Acknowledgments. -- PART I: FOUNDATIONS OF NEURAL NETWORKS. -- Neural Systems: An Introduction. -- Biological Foundations of Neuronal Morphology. -- Neural Units: Concepts, Models, and Learning. -- PART II: STATIC NEURAL NETWORKS. -- Multilayered Feedforward Neural Networks (MFNNs) and Backpropagation Learning Algorithms. -- Advanced Methods for Learning Adaptation in MFNNs. -- Radial Basis Function Neural Networks. -- Function Approximation Using Feedforward Neural Networks. -- PART III: DYNAMIC NEURAL NETWORKS. -- Dynamic Neural Units (DNUs): Nonlinear Models and Dynamics. -- Continuous-Time Dynamic Neural Networks. -- Learning and Adaptation in Dynamic Neural Networks. -- Stability of Continuous-Time Dynamic Neural Networks. -- Discrete-Time Dynamic Neural Networks and Their Stability. -- PART IV: SOME ADVANCED TOPICS IN NEURAL NETWORKS. -- Binary Neural Networks. -- Feedback Binary Associative Memories. -- Fuzzy Sets and Fuzzy Neural Networks. -- References and Bibliography. -- Appendix A: Current Bibliographic Sources on Neural Networks. -- Index. |
| Record Nr. | UNINA-9910830448903321 |
|
Gupta Madan M.
|
||
| [Hoboken, New Jersey] : , : Wiley, , 2003 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Systems engineering neural networks / / Alessandro Migliaccio, Giovanni Iannone
| Systems engineering neural networks / / Alessandro Migliaccio, Giovanni Iannone |
| Autore | Migliaccio Alessandro |
| Pubbl/distr/stampa | Hoboken, NJ : , : John Wiley & Sons, Inc., , [2023] |
| Descrizione fisica | 1 online resource (243 pages) |
| Disciplina | 006.3/2 |
| Soggetto topico |
Neural networks (Computer science)
Computer simulation Systems engineering |
| ISBN |
1-119-90202-9
1-119-90200-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Title Page -- Copyright -- Contents -- About the Authors -- Acknowledgements -- How to Read this Book -- Part I Setting the Scene -- Chapter 1 A Brief Introduction -- 1.1 The Systems Engineering Approach to Artificial Intelligence (AI) -- 1.2 Chapter Summary -- Questions -- Chapter 2 Defining a Neural Network -- 2.1 Biological Networks -- 2.2 From Biology to Mathematics -- 2.3 We Came a Full Circle -- 2.4 The Model of McCulloch‐Pitts -- 2.5 The Artificial Neuron of Rosenblatt -- 2.6 Final Remarks -- 2.7 Chapter Summary -- Questions -- Sources -- Chapter 3 Engineering Neural Networks -- 3.1 A Brief Recap on Systems Engineering -- 3.2 The Keystone: SE4AI and AI4SE -- 3.3 Engineering Complexity -- 3.4 The Sport System -- 3.5 Engineering a Sports Club -- 3.6 Optimization -- 3.7 An Example of Decision Making -- 3.8 Futurism and Foresight -- 3.9 Qualitative to Quantitative -- 3.10 Fuzzy Thinking -- 3.11 It Is all in the Tools -- 3.12 Chapter Summary -- Questions -- Sources -- Part II Neural Networks in Action -- Chapter 4 Systems Thinking for Software Development -- 4.1 Programming Languages -- 4.2 One More Thing: Software Engineering -- 4.3 Chapter Summary -- Questions -- Source -- Chapter 5 Practice Makes Perfect -- 5.1 Example 1: Cosine Function -- 5.2 Example 2: Corrosion on a Metal Structure -- 5.3 Example 3: Defining Roles of Athletes -- 5.4 Example 4: Athlete's Performance -- 5.5 Example 5: Team Performance -- 5.5.1 A Human‐Defined‐System -- 5.5.2 Human Factors -- 5.5.3 The Sports Team as System of Interest -- 5.5.4 Impact of Human Error on Sports Team Performance -- 5.5.4.1 Dataset -- 5.5.4.2 Problem Statement -- 5.5.4.3 Feature Engineering and Extraction -- 5.5.4.4 Creation of Computed Columns -- 5.5.4.5 Explorative Data Analysis (EDA) -- 5.5.4.6 Extension ‐ Sampling Method for an Imbalanced Dataset.
5.5.4.7 Building a Neural Network Model -- 5.5.4.8 Training Outcome and Model Evaluation -- 5.5.4.9 Evaluate Using Test Data -- 5.6 Example 6: Trend Prediction -- 5.7 Example 7: Symplex and Game Theory -- 5.8 Example 8: Sorting Machine for Lego® Bricks -- 5.8.1 Challenge for Readers -- Part III Down to the Basics -- Chapter 6 Input/Output, Hidden Layer and Bias -- 6.1 Input/Output -- 6.2 Hidden Layer -- 6.2.1 How Many Hidden Nodes Should we Have? -- 6.3 Bias -- 6.4 Final Remarks -- 6.5 Chapter Summary -- Questions -- Source -- Chapter 7 Activation Function -- 7.1 Types of Activation Functions -- 7.2 Activation Function Derivatives -- 7.3 Activation Functions Response to W and b Variables -- 7.4 Final Remarks -- 7.5 Chapter Summary -- Questions -- Source -- Chapter 8 Cost Function, Back‐Propagation and Other Iterative Methods -- 8.1 What Is the Difference between Loss and Cost? -- 8.2 Training the Neural Network -- 8.3 Back‐Propagation (BP) -- 8.4 One More Thing: Gradient Method and Conjugate Gradient Method -- 8.5 One More Thing: Newton's Method -- 8.6 Chapter Summary -- Questions -- Sources -- Chapter 9 Conclusions and Future Developments -- Glossary and Insights -- Index -- EULA. |
| Record Nr. | UNINA-9910830792103321 |
|
Migliaccio Alessandro
|
||
| Hoboken, NJ : , : John Wiley & Sons, Inc., , [2023] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Wavelet neural networks : with applications in financial engineering, chaos, and classification / / Antonis K. Alexandridis, Achilleas D. Zapranis
| Wavelet neural networks : with applications in financial engineering, chaos, and classification / / Antonis K. Alexandridis, Achilleas D. Zapranis |
| Autore | Alexandridis Antonis K. |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : John Wiley & Sons, , 2014 |
| Descrizione fisica | 1 online resource (263 p.) |
| Disciplina | 006.3/2 |
| Soggetto topico |
Wavelets (Mathematics)
Neural networks (Computer science) Financial engineering |
| ISBN |
1-118-59550-5
1-118-59627-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Wavelet Neural Networks; Contents; Preface; 1 Machine Learning and Financial Engineering; Financial Engineering; Financial Engineering and Related Research Areas; Functions of Financial Engineering; Applications of Machine Learning in Finance; From Neural to Wavelet Networks; Wavelet Analysis; Extending the Fourier Transform: The Wavelet Analysis Paradigm; Neural Networks; Wavelet Neural Networks; Applications of Wavelet Neural Networks in Financial Engineering, Chaos, and Classification; Building Wavelet Networks; Variable Selection; Model Selection; Model Adequacy Testing; Book Outline
References2 Neural Networks; Parallel Processing; Processing Units; Activation Status and Activation Rules; Connectivity Model; Perceptron; The Approximation Theorem; The Delta Rule; Backpropagation Neural Networks; Multilayer Feedforward Networks; The Generalized Delta Rule; Backpropagation in practice; Training with Backpropagation; Network Paralysis; Local Minima; Nonunique Solutions; Configuration Reference; Conclusions; References; 3 Wavelet Neural Networks; Wavelet Neural Networks for Multivariate Process Modeling; Structure of a Wavelet Neural Network Initialization of the Parameters of the Wavelet NetworkTraining a Wavelet Network with Backpropagation; Stopping Conditions for Training; Evaluating the Initialization Methods; Conclusions; References; 4 Model Selection: Selecting the Architecture of the Network; The Usual Practice; Early Stopping; Regularization; Pruning; Minimum Prediction Risk; Estimating the Prediction Risk Using Information Criteria; Estimating the Prediction Risk Using Sampling Techniques; Bootstrapping; Cross-Validation; Model Selection Without Training; Evaluating the Model Selection Algorithm Case 1: Sinusoid and Noise with Decreasing VarianceCase 2: Sum of Sinusoids and Cauchy Noise; Adaptive Networks and Online synthesis; Conclusions; References; 5 Variable Selection: Determining the Explanatory Variables; Existing Algorithms; Sensitivity Criteria; Model Fitness Criteria; Algorithm for Selecting the Significant Variables; Resampling Methods for the Estimation of Empirical Distributions; Evaluating the Variable Significance Criteria; Case 1: Sinusoid and Noise with Decreasing Variance; Case 2: Sum of Sinusoids and Cauchy Noise; Conclusions; References 6 Model Adequacy: Determining a Networks Future PerformanceTesting the residuals; Testing for Serial Correlation in the Residuals; Evaluation criteria for the prediction ability of the wavelet network; Measuring the Accuracy of the Predictions; Scatter Plots; Linear Regression Between Forecasts and Targets; Measuring the Ability to Predict the Change in Direction; Two simulated Cases; Case 1: Sinusoid and Noise with Decreasing Variance; Case 2: Sum of Sinusoids and Cauchy Noise; Classification; Assumptions and Objectives of Discriminant Analysis; Validation of the Discriminant Function Evaluating the Classification Ability of a Wavelet Network |
| Record Nr. | UNINA-9910141724303321 |
|
Alexandridis Antonis K.
|
||
| Hoboken, New Jersey : , : John Wiley & Sons, , 2014 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Wavelet neural networks : with applications in financial engineering, chaos, and classification / / Antonis K. Alexandridis, Achilleas D. Zapranis
| Wavelet neural networks : with applications in financial engineering, chaos, and classification / / Antonis K. Alexandridis, Achilleas D. Zapranis |
| Autore | Alexandridis Antonis K. |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : John Wiley & Sons, , 2014 |
| Descrizione fisica | 1 online resource (263 p.) |
| Disciplina | 006.3/2 |
| Soggetto topico |
Wavelets (Mathematics)
Neural networks (Computer science) Financial engineering |
| ISBN |
1-118-59550-5
1-118-59627-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Wavelet Neural Networks; Contents; Preface; 1 Machine Learning and Financial Engineering; Financial Engineering; Financial Engineering and Related Research Areas; Functions of Financial Engineering; Applications of Machine Learning in Finance; From Neural to Wavelet Networks; Wavelet Analysis; Extending the Fourier Transform: The Wavelet Analysis Paradigm; Neural Networks; Wavelet Neural Networks; Applications of Wavelet Neural Networks in Financial Engineering, Chaos, and Classification; Building Wavelet Networks; Variable Selection; Model Selection; Model Adequacy Testing; Book Outline
References2 Neural Networks; Parallel Processing; Processing Units; Activation Status and Activation Rules; Connectivity Model; Perceptron; The Approximation Theorem; The Delta Rule; Backpropagation Neural Networks; Multilayer Feedforward Networks; The Generalized Delta Rule; Backpropagation in practice; Training with Backpropagation; Network Paralysis; Local Minima; Nonunique Solutions; Configuration Reference; Conclusions; References; 3 Wavelet Neural Networks; Wavelet Neural Networks for Multivariate Process Modeling; Structure of a Wavelet Neural Network Initialization of the Parameters of the Wavelet NetworkTraining a Wavelet Network with Backpropagation; Stopping Conditions for Training; Evaluating the Initialization Methods; Conclusions; References; 4 Model Selection: Selecting the Architecture of the Network; The Usual Practice; Early Stopping; Regularization; Pruning; Minimum Prediction Risk; Estimating the Prediction Risk Using Information Criteria; Estimating the Prediction Risk Using Sampling Techniques; Bootstrapping; Cross-Validation; Model Selection Without Training; Evaluating the Model Selection Algorithm Case 1: Sinusoid and Noise with Decreasing VarianceCase 2: Sum of Sinusoids and Cauchy Noise; Adaptive Networks and Online synthesis; Conclusions; References; 5 Variable Selection: Determining the Explanatory Variables; Existing Algorithms; Sensitivity Criteria; Model Fitness Criteria; Algorithm for Selecting the Significant Variables; Resampling Methods for the Estimation of Empirical Distributions; Evaluating the Variable Significance Criteria; Case 1: Sinusoid and Noise with Decreasing Variance; Case 2: Sum of Sinusoids and Cauchy Noise; Conclusions; References 6 Model Adequacy: Determining a Networks Future PerformanceTesting the residuals; Testing for Serial Correlation in the Residuals; Evaluation criteria for the prediction ability of the wavelet network; Measuring the Accuracy of the Predictions; Scatter Plots; Linear Regression Between Forecasts and Targets; Measuring the Ability to Predict the Change in Direction; Two simulated Cases; Case 1: Sinusoid and Noise with Decreasing Variance; Case 2: Sum of Sinusoids and Cauchy Noise; Classification; Assumptions and Objectives of Discriminant Analysis; Validation of the Discriminant Function Evaluating the Classification Ability of a Wavelet Network |
| Record Nr. | UNINA-9910814775103321 |
|
Alexandridis Antonis K.
|
||
| Hoboken, New Jersey : , : John Wiley & Sons, , 2014 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
