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Microsoft Windows Help File Content  |  2002-03-08  |  2.7 KB  |  41 lines
  1. :Base neural and adaptive systems.HLP
  2. 1 Neural and Adaptive Systems - Preface
  3. 2 Preface=Neural_Systems_Fundamentals_Through_Simulations
  4. 1 Chapter 1 - Data Fitting with Linear Models
  5. 2 Chapter 1- Data Fitting with Linear Models=Chapter_1-_Data_Fitting_with_Linear_Models
  6. 2 1.1 Introduction=1.1_Introduction
  7. 2 1.2 Linear Models=1.2_Linear_Models
  8. 2 1.3 Least Squares=1.3_Least_Squares
  9. 2 1.4 Adaptive Linear Systems=1.4_Adaptive_Linear_Systems
  10. 2 1.5 Estimation of the Gradient The LMS Algorithm=1.5_Estimation_of_the_Gradient_The_LMS_Algorithm
  11. 2 1.6 A Methodology for Stable Adaptation=1.6_A_Methodology_for_Stable_Adaptation
  12. 2 1.7 Regression for Multiple Variables=1.7_Regression_for_Multiple_Variables
  13. 2 1.8 Newton's Method=1.8_Newtons_Method
  14. 2 1.9 Analytic versus Iterative Solutions=1.9_Analytic_versus_Iterative_Solutions
  15. 2 1.10 The Linear Regression Model=1.10_The_Linear_Regression_Model
  16. 2 1.11 Conclusions=1.11_Conclusions
  17. 2 1.12 Exercises=1.12_Exercises
  18. 2 1.13 NeuroSolutions Examples=1.13_NeuroSolutions_Examples
  19. 2 1.14 Concept Map for Chapter 1=1.14_Concept_Map_for_Chapter_1
  20. 1 Chapters 2-11
  21. 2 Chapter 2 - Pattern Recognition=Chapter_2
  22. 2 Chapter 3 - Multilayer Perceptrons=Chapter_3_-_Multilayer_Perceptrons
  23. 2 Chapter 4 - Designing and Training MLPs=Chapter_4_-_Designing_and_Training_MLPs
  24. 2 Chapter 5 - Function Approximation with MLPs, RBFs, and Support Vector Machines=Chapter_5_-_Function_Approximation_with_MLPs,_RBFs,_and_Support_Vector_Machines
  25. 2 Chapter 6 - Hebbian Learning and Principal Component Analysis=Chapter_6_-_Hebbian_Learning_and_Principal_Component_Analysis
  26. 2 Chapter 7 - Competitive and Kohonen Networks=Chapter_7_-_Competitive_and_Kohonen_Networks
  27. 2 Chapter 8 - Principles of Digital Signal Processing=Chapter_8_-_Principles_of_Digital_Signal_Processing
  28. 2 Chapter 9 - Adaptive Filters=Chapter_9_-_Adaptive_Filters
  29. 2 Chapter 10 - Temporal Processing with Neural Networks=Chapter_10_-_Temporal_Processing_with_Neural_Networks
  30. 2 Chapter 11 - Training and Using Recurrent Networks=Chapter_11_-_Training_and_Using_Recurrent_Networks
  31. 2 Appendix A - Elements of Linear Algebra Pattern Recognition=Appendix_A_-_Elements_of_Linear_Algebra_Pattern_Recognition
  32. 1 appendixb
  33. 2 Appendix B - NeuroSolutions Tutorial=Appendix_B_-_NeuroSolutions_Tutorial
  34. 2 B.1 Introduction to NeuroSolutions=B.1_Introduction_to_NeuroSolutions
  35. 2 B.2 Introduction to the Interactive Examples=B.2_Introduction_to_the_Interactive_Examples
  36. 2 B.3 Basic Operation of NeuroSolutions=B.3_Basic_Operation_of_NeuroSolutions
  37. 2 B.4 Probing the System=B.4_Probing_the_System
  38. 2 B.5 The Input Family=B.5_The_Input_Family
  39. 2 B.6 Training a Network=B.6_Training_a_Network
  40. 2 B.7 Summary=B.7_Summary
  41.