LEADER 05042oam 2200517 450 001 9910821027103321 005 20190911112729.0 010 $a981-4522-74-0 035 $a(OCoLC)857066058 035 $a(MiFhGG)GVRL8RHI 035 $a(EXLCZ)992550000001107685 100 $a20140419h20132013 uy 0 101 0 $aeng 135 $aurun|---uuuua 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aPrinciples of artificial neural networks /$fDaniel Graupe, University of Illinois, Chicago, USA 205 $a3rd ed. 210 $aSingapore ;$aHackensack, N.J. $cWorld Scientific$d2013 210 1$aNew Jersey :$cWorld Scientific,$d[2013] 210 4$d?2013 215 $a1 online resource (xviii, 363 pages) $cillustrations (some color) 225 1 $aAdvanced series in circuits and systems ;$vv. 7 300 $aDescription based upon print version of record. 311 $a981-4522-73-2 311 $a1-299-77093-2 320 $aIncludes bibliographical references and indexes. 327 $aAcknowledgments; 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 327 $a4.1.1. 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 327 $aChapter 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) 327 $a6.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 327 $a7.8. 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 327 $a7.B.6. Concluding discussion 330 $aArtificial neural networks are most suitable for solving problems that are complex, ill-defined, highly nonlinear, of many and different variables, and/or stochastic. Such problems are abundant in medicine, in finance, in security and beyond. This volume covers the basic theory and architecture of the major artificial neural networks. Uniquely, it presents 18 complete case studies of applications of neural networks in various fields, ranging from cell-shape classification to micro-trading in finance and to constellation recognition - all with their respective source codes. These case studies d 410 0$aAdvanced series on circuits and systems ;$vv. 7. 606 $aNeural networks (Computer science) 615 0$aNeural networks (Computer science) 676 $a006.32 700 $aGraupe$b Daniel$014109 801 0$bMiFhGG 801 1$bMiFhGG 906 $aBOOK 912 $a9910821027103321 996 $aPrinciples of artificial neural networks$94029541 997 $aUNINA