Matlab's Neural Network Toolbox User’s Guide2014b

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matlab 神经网络工具箱2014b Matlab's Neural Network Toolbox User’s Guide
y June 1992 First printing April 1993 Second printing January 1997 Third printing July 1997 Fourth printing January 1998 Fifth printing Revised for Version 3( Release 11) September 2000 Sixth printing Revised for Version 4(Release 12) June 2001 Seventh printing Minor revisions release 12.1 July 2002 Online onl Minor revisions Release 13) January 2003 Online only Minor revisions (release 13SP1) June 2004 Online only Revised for Version 4.0.3(Release 14) October 2004 Online onl Revised for Version 4.0.4(Release 14SP1) October 2004 Eighth printin March 2005 Online only8 Revised for Version 4. 0.4 Revised for Version 4.0.5(Release 14SP2) March 2006 Online onl, Revised for Version 5.0(Release 2006a) September 2006 Ninth printing Minor revisions (release 2006b) March 2007 Online only Minor revisions(release 2007a) September 2007 Online only Revised for Version 5. 1( Release 2007b) March 2008 Online only Revised for Version 6.0 Release 2008a) October 2008 Online only Revised for Version 6.0.1(Release 2008b) March 2009 Online only Revised for Version 6.0.2(Release 2009a) September 2009 Online only Revised for Version 6.0.3(Release 2009b) March 2010 Online only Revised for Version 6.0.4( Release 2010a) September 2010 Online only Revised for Version 7.0( Release 2010b) April 2011 Online only Revised for Version 7.0.1(Release 2011a) September 2011 Online only Revised for Version 7.0.2 (Release 2011b) March 2012 Online only Revised for Version 7.0.3 (Release 2012a September 2012 Online only Revised for Version 8.0 (Release 2012b) March 2013 Online only Revised for Version 8.0.1Release 2013a September 2013 Online only Revised for Version 8. 1(Release 2013b) March 2014 Online only Revised for Version 8.2 (Release 2014a October 2014 Online only Revised for Version 8.2.1(Release 2014b) Contents Neural Network Toolbox Design Book Neural Network Objects, Data, and Training Styles Workflow for Neural Network Design 1-2 Four Levels of Neural Network design 1-4 Neuron model Simple neuron 1-5 Transfer functions 1-6 Neuron with Vector Input Neural Network Architectures 1-11 One Layer of Neurons 1-11 Multiple layers of Neurons 1-13 Input and Output Processing Functions 1-15 Create Neural Network Object Configure Neural Network Inputs and Outputs 1-21 Understanding Neural Network Toolbox Data Structures 1-23 Simulation with Concurrent Inputs in a Static Network 1-23 Simulation with Sequential Inputs in a Dynamic Network 1-24 Simulation with Concurrent Inputs in a Dynamic Network. 1-26 Neural Network Training Concepts 1-28 Incremental Training with adapt 1-28 Batch Training 1-30 Training Feedback 1-33 Multilayer Neural Networks and Backpropagation Training 2 Multilayer Neural Networks and Backpropagation Training 2-2 Multilayer Neural Network Architecture 2-4 Neuron Model (logsig, tansig, purelin) 2-4 Feedforward Neural Network 2-5 Prepare Data for Multilayer Neural Networks 2-8 Choose Neural Network Input-Output Processing Functions Representing Unknown or Don't-Care Targets 2-9 2-11 Divide data for Optimal Neural Network training 2-12 Create, Configure, and Initialize Multilayer Neural Networks 2-14 Other related Architectures 2-15 Initializing Weights(init) 2-15 Train and Apply multilayer Neural Networks 2-17 Training algorithms 2-18 Training Example 2-20 Use the network 2-22 Analyze Neural Network Performance After Training 2-23 Improving Results 2-28 Limitations and cautions 2-29 Contents Dynamic Neural Networks 3 Introduction to Dynamic Neural Networks 3-2 How Dynamic Neural Networks Work 3-3 Feedforward and recurrent neural networks 3-3 Applications of Dynamic Networks 3-11 Dynamic Network Structures 3-11 Dynamic Network Training 3-12 Design Time Series Time-Delay Neural Networks 3-14 Prepare Input and Layer Delay States 3-18 Design Time Series Distributed Delay Neural Networks.. 3-20 Design Time Series NARX Feedback Neural Networks 3-23 Multiple external Variables.............3-30 Design Layer-Recurrent Neural Networks 3-31 Create Reference Model Controller with MATLAB Script 3-34 Multiple Sequences with Dynamic Neural Networks 3-41 Neural Network Time-Series Utilities 3-42 Train Neural Networks with Error Weights 3-44 Normalize Errors of Multiple Outputs 3-47 Multistep Neural Network Prediction 3-52 Up in Open-Loop Mode 3-52 Multistep Closed-Loop Prediction From Initial Conditions 3-53 Multistep Closed-Loop Prediction Following Known queI 3-53 Following Closed-Loop Simulation with Open-Loop Simulation 3-54 Control Systems Introduction to Neural Network Control Systems 4-2 Design Neural Network Predictive Controller in Simulink 44 ystem Identification 4-4 Predictive Control 4-5 Use the Neural network predictive Controller block 4-6 Design NARMA-L2 Neural Controller in Simulink 4-14 Identification of the Narma-l2 Model 4-14 NARMA-L2 Controller 4-16 Use the NARMA-L2 Controller block 4-18 Design Model-Reference Neural Controller in Simulink 4-23 Use the model reference Controller block 4-24 Import-Export Neural Network Simulink Control Systems 4-31 Import and Export Networks 4-31 Import and Export Training Data 4-35 Radial Basis neural networks 5 Introduction to Radial basis Neural networks 5-2 Important Radial Basis Functions Radial basis Neural networks 5-3 Neuron model 5-3 Network architecti 5-4 Exact Design (newrbe) 5-6 More Efficient Design (newrb Examples 5-8 Probabilistic Neural networks 5-10 Network Architecture 5-10 Design (newpnn) 5-11 Contents Generalized Regression Neural Networks 5-13 Network Architecture 5-13 Design(newgrnn 5-15 Self-Organizing and Learning Vector Quantization Networks 6 Introduction to Self-Organizing and Lvq 6-2 Important Self-Organizing and LvQ Functions 6-2 Cluster with a Competitive Neural Network 6-3 Architecture 6-3 Create a Competitive Neural Network 6-4 Kohonen Learning Rule (learnk 6-5 Bias Learning rule dlearncon) 6-5 Training 6-6 Graphical Example 6-8 Cluster with Self-Organizing Map Neural Network 6-9 Topologies(gridtop, hextop, randtop 6-11 Distance Functions(dist, linkdist, mandis, boxdist) 6-14 Architecture 6-17 Create a Self-Organizing Map Neural Network(selforgmap) 6-18 Training (learnsomb) 6-19 E 6-22 Learning Vector quantization (LvQ) Neural Networks 6-34 Architecti 6-34 Creating an LvQ Network 6-35 LvQ1 Learning Rule (learnlv1) 6-38 Training 6-39 Supplemental lvQ2. 1 Learning Rule (learnlv2 6-41 Adaptive Filters and Adaptive Training 7 Adaptive Neural Network Filters 7-2 Adapt p tive functions 7-2 Linear neuron model 7-3 Adaptive Linear Network Architecture 7-3 ast mean Square error 7-6 LMS Algorithm (learnwh) 7-7 Adaptive Filtering(adapt 7-7 Advanced Topics Neural Networks with Parallel and gPu computing 8-2 Modes of parallelism 8-2 Distributed Computing 8-3 Single gPu computing 8-5 Distributed GPu Computing 8-8 Parallel Time Series 8-9 Parallel availability, Fallbacks, and Feedback 8-10 Optimize Neural Network Training Speed and Memory 8-12 Memory reduction 8-12 Fast Elliot Sigmoid 8-12 Choose a Multilayer Neural Network Training Function 8-16 siN Data set 8-17 PARITY Data set 8-19 ENGINE Data set 8-21 CANCER Data set 8-23 CHOLESTEROL Data set 8-25 DIABETES Data Set 8-27 Summary 8-29 Improve Neural Network Generalization and Avoid Overfitting 8-31 Retraining neural Networks 8-32 Multiple neural Networks 8-34 Content

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