Principles of artificial neural networks / / Daniel Graupe, University of Illinois, Chicago, USA

• Autore: Graupe Daniel
• Pubblicazione: Singapore ; ; Hackensack, N.J., : World Scientific, 2013; New Jersey : , : World Scientific, , [2013]; �2013
• Edizione: 3rd ed.
• Descrizione fisica: 1 online resource (xviii, 363 pages) : illustrations (some color)
• Disciplina: 006.32
• Soggetto topico: Neural networks (Computer science)
• Note generali: Description based upon print version of record.
• Nota di bibliografia: Includes bibliographical references and indexes.
• 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
• Sommario/riassunto: Artificial neural networks are most suitable for solving problems that are complex, ill-defined, highly nonlinear, of many and different variables, and/or stochastic. Such problems are abundant in medicine, in finance, in security and beyond. This volume covers the basic theory and architecture of the major artificial neural networks. Uniquely, it presents 18 complete case studies of applications of neural networks in various fields, ranging from cell-shape classification to micro-trading in finance and to constellation recognition - all with their respective source codes. These case studies d
• Titolo autorizzato: Principles of artificial neural networks
• ISBN: 981-4522-74-0
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910821027103321
• Lo trovi qui: Univ. Federico II
• Serie: Advanced series on circuits and systems ; ; v. 7.