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数据标准化处理.m
资源名称：rbf 网络设计实例.rar　[点击查看]
上传用户：yetwld
上传日期：2010-01-26
资源大小：82k
文件大小：6k
源码类别：

TAPI编程

开发平台：

Matlab

数据标准化处理.m：源码内容
  1. %数据标准化处理
  2. %未标准化的建模数据集
  3. mg_data=[0.093094 -0.57393 -0.87823 -0.52121 -0.06666 2.3245;
  4. -0.32246 0.94733 -0.87823 -0.68983 0.62931 -0.60889;
  5. -0.32246 -0.32039 -0.52929 -0.35258 -0.41465 -0.47555;
  6. 2.5864 -0.32039 -1.2272 -1.0271 -0.67564 -0.34221;
  7. 0.093094 0.94733 0.86645 -0.35258 -0.41465 -0.74222;
  8. -0.53023 -0.066843 -1.2272 -0.68983 -0.50164 -0.60889;
  9. -0.32246 0.69379 -0.52929 -0.18396 0.19433 -0.47555;
  10. -0.11468 0.69379 0.16858 -0.52121 -1.2846 -1.4089;
  11. -0.53023 -0.82747 0.16858 -0.85846 0.45532 -0.47555;
  12. -0.73801 -0.066843 0.16858 -0.01533 0.020337 -0.47555;
  13. -0.53023 0.94733 0.16858 -1.0271 -0.50164 -0.20887;
  14. -0.53023 0.69379 -0.52929 -0.68983 0.36832 -0.74222;
  15. -0.11468 0.94733 0.16858 -0.35258 -0.50164 -0.47555;
  16. -0.73801 0.94733 0.16858 -0.35258 -0.84963 -0.87556;
  17. -0.53023 0.94733 -1.925 -1.1957 -1.3716 -3.009;
  18. -0.73801 0.69379 -0.87823 -0.68983 -0.41465 -2.7423;
  19. -0.32246 -1.8416 -0.87823 -0.85846 -0.50164 2.1912;
  20. 2.7942 -0.57393 -1.5761 -0.35258 -1.0236 -1.1422;
  21. 0.92419 -0.32039 -1.925 -1.3643 -0.84963 -1.4089;
  22. 0.093094 -0.57393 -0.87823 -1.533 -1.1976 -1.2756;
  23. -0.53023 0.44024 -0.87823 -0.68983 -0.67564 -1.0089;
  24. 0.30087 0.1867 0.51751 -0.85846 -0.76263 0.45782;
  25. -0.73801 0.94733 0.86645 -0.52121 -1.0236 0.45782;
  26. 0.30087 0.69379 0.51751 -1.1957 -1.0236 0.99117;
  27. -0.73801 -0.066843 0.16858 -0.52121 -0.58864 1.1245;
  28. -0.53023 1.2009 0.16858 -1.0271 -0.84963 0.32448;
  29. -0.94578 0.1867 0.86645 -1.0271 -1.1976 0.45782;
  30. -0.11468 0.1867 0.16858 -0.01533 -0.50164 0.99117;
  31. -0.32246 1.2009 -0.18036 -1.1957 -1.5456 -0.47555;
  32. -0.11468 0.44024 -0.18036 -1.7016 -1.4586 -0.075535;
  33. -0.32246 -0.32039 -1.2272 -0.52121 -1.1976 -0.34221;
  34. -0.53023 0.69379 0.86645 -0.85846 -0.84963 0.99117;
  35. -0.94578 1.4544 0.86645 -1.533 -1.1976 -0.20887;
  36. 0.30087 0.1867 0.16858 -1.0271 -1.3716 0.59115;
  37. -0.73801 0.1867 -0.52929 -0.68983 -0.84963 0.45782;
  38. -0.53023 1.2009 0.86645 -0.01533 -0.84963 -1.2756;
  39. -0.11468 0.94733 1.9133 -0.52121 -1.5456 0.72449;
  40. -0.73801 1.708 0.86645 -0.68983 -1.0236 -0.74222;
  41. -0.53023 0.1867 -1.2272 -0.68983 -0.41465 0.32448;
  42. -0.53023 0.69379 0.86645 -0.68983 -0.67564 -0.20887;
  43. -0.94578 1.4544 -0.18036 -1.7016 -0.84963 -0.075535;
  44. -0.94578 0.69379 1.5643 -0.68983 0.10733 1.3912;
  45. -0.94578 -1.081 0.86645 -0.52121 -0.41465 0.59115;
  46. -0.94578 0.94733 0.86645 -0.35258 -0.76263 -0.60889;
  47. 3.0019 0.69379 -1.5761 0.32192 0.45532 -0.34221;
  48. 1.9631 0.1867 -0.87823 -0.35258 -0.32765 1.2578;
  49. -0.73801 0.44024 1.9133 2.0082 1.4123 0.057803;
  50. 3.6253 0.1867 -1.5761 1.1651 0.19433 -1.1422;
  51. 4.0408 -1.3346 -1.925 -0.01533 -0.50164 -0.075535;
  52. 0.92419 0.1867 0.16858 -0.01533 0.19433 -0.47555;
  53. -0.94578 -0.066843 -0.52929 0.99643 1.5863 2.1912;
  54. -0.53023 0.1867 -0.87823 -0.68983 -0.32765 -1.1422;
  55. -0.94578 -0.57393 -0.87823 2.0082 2.1083 0.45782;
  56. -0.32246 -0.57393 -0.87823 2.3454 2.1083 0.99117;
  57. -0.94578 -1.3346 0.86645 2.8513 2.4562 -0.34221;
  58. -0.73801 -1.8416 -0.52929 2.3454 2.2822 0.21485;
  59. 0.92419 1.2009 0.86645 0.49055 0.71631 -0.87556;
  60. 1.5475 -0.82747 -0.18036 0.99643 -0.15366 0.057803;
  61. -0.53023 -1.5881 -1.5761 2.0082 1.9343 -0.075535;
  62. -0.11468 -1.3346 -0.52929 2.5141 1.2383 0.85783;
  63. -0.73801 -0.066843 0.86645 0.99643 1.3253 -0.60889;
  64. 1.5475 -0.066843 0.16858 0.65918 0.71631 -0.74222;
  65. -0.94578 0.44024 1.5643 0.99643 1.2383 2.1912;
  66. 0.30087 0.94733 1.9133 -1.0271 -0.15366 0.99117;
  67. -0.11468 -0.066843 0.86645 -0.01533 0.020337 -0.47555;
  68. 2.1708 1.2009 -0.18036 -0.85846 -0.76263 -0.87556;
  69. 2.7942 -1.5881 1.2154 1.3337 0.8903 0.32448;
  70. 0.92419 0.1867 -0.52929 -0.35258 -0.32765 -1.4089;
  71. 1.3397 -2.6023 0.16858 1.3337 1.2383 -0.075535;
  72. -0.11468 -1.3346 -1.5761 2.5141 1.5863 -0.34221;
  73. -0.32246 -0.57393 -2.6229 -0.68983 -0.50164 -0.87556;
  74. 0.71642 -0.32039 0.16858 0.65918 2.1083 0.45782;
  75. 0.92419 -0.57393 -0.18036 -0.35258 -0.41465 -0.34221;
  76. 0.50864 -1.3346 -0.52929 1.3337 1.5863 -0.60889;
  77. -0.11468 -1.8416 0.51751 1.3337 1.5863 -0.09035;
  78. -0.73801 -1.8416 -0.18036 -0.35258 -0.50164 1.3912;
  79. 0.50864 -0.57393 -0.18036 0.1533 0.020337 0.99117;
  80. 1.5475 -2.6023 -0.87823 -0.01533 0.36832 0.99117;
  81. 1.3397 -0.32039 -0.52929 1.3337 2.2822 -0.20887;
  82. 0.093094 -1.3346 -1.5761 1.6709 2.1083 0.85783;
  83. 0.093094 0.69379 0.86645 0.1533 0.71631 -1.4089;
  84. 0.92419 -0.82747 0.16858 0.32192 0.10733 -0.20887;
  85. -0.11468 -0.57393 0.86645 0.8278 1.0643 -0.20887;
  86. 0.71642 -0.57393 -0.52929 1.6709 1.4123 -0.20887;
  87. 0.093094 0.1867 -1.2272 1.3337 2.0213 -0.74222;
  88. -0.94578 -1.3346 1.2154 -0.01533 -0.84963 -0.74222;
  89. -0.73801 -0.066843 0.86645 0.65918 0.71631 -1.1422;
  90. -0.32246 -1.3346 0.51751 0.32192 0.71631 -0.20887;
  91. 0.50864 -1.8416 -1.2272 0.99643 2.1083 0.59115;
  92. -0.53023 -0.82747 0.16858 0.32192 1.7603 0.19114;
  93. -0.73801 0.69379 1.2154 0.32192 -0.15366 -2.2089;
  94. -0.94578 0.69379 0.86645 1.3337 1.4123 -1.8089;
  95. -0.32246 -0.32039 2.9601 -0.18396 0.36832 -1.2459;
  96. -0.53023 0.69379 2.2622 -0.18396 -0.41465 -0.87556;
  97. -0.73801 -0.82747 0.51751 0.65918 0.36832 0.19114;
  98. 0.92419 -0.57393 0.16858 -0.52121 -0.15366 -0.47555;
  99. 3.4175 -0.32039 -0.52929 -0.52121 -0.32765 -0.47555;
  100. -0.94578 0.44024 1.5643 0.65918 0.45532 -1.1422;
  101. -0.53023 0.44024 0.51751 0.32192 -0.41465 -1.4089;
  102. -0.11468 0.1867 1.2154 0.65918 0.020337 -2.3423;
  103. 0.30087 0.69379 0.16858 -0.35258 0.54232 0.99117;
  104. -0.73801 -0.066843 0.16858 -0.35258 0.19433 0.32448;
  105. 0.30087 1.2009 1.5643 0.8278 0.54232 -0.34221;
  106. -0.73801 0.94733 1.2154 -0.01533 -0.15366 -1.8089;
  107. -0.53023 0.69379 1.2154 0.99643 0.45532 0.99117;
  108. -0.53023 0.94733 0.86645 0.32192 1.0643 -0.74222;
  109. 0.093094 0.1867 1.2154 -0.01533 -0.32765 1.2578;
  110. 0.71642 -0.066843 0.16858 -0.52121 -0.06666 0.45782;
  111. -0.53023 -0.066843 1.2154 -0.01533 -0.58864 0.45782;
  112. -0.32246 -0.32039 0.16858 0.49055 -0.06666 -0.74222;
  113. 0.92419 -0.32039 -0.18036 -0.35258 -0.32765 0.057803;
  114. -0.73801 -0.32039 0.16858 -0.35258 -0.58864 -0.20887;
  115. 0.093094 -0.82747 0.16858 2.0082 1.2383 1.3912;
  116. -0.73801 -1.8416 0.16858 0.8278 0.8903 0.59115;
  117. -0.73801 -2.3487 -2.6229 0.65918 0.54232 0.19114;
  118. -0.32246 -1.8416 -1.2272 1.1651 1.0643 1.2578;
  119. 0.093094 -0.82747 -1.5761 0.99643 1.2383 0.32448;
  120. 1.9631 -2.0952 -0.52929 2.1768 1.4123 0.99117;
  121. 0.92419 -0.066843 -1.5761 1.1651 0.8903 -0.34221;
  122. 0.50864 -0.57393 -1.5761 0.49055 1.2383 1.1245;
  123. 0.92419 -1.081 -1.2272 0.1533 -0.84963 0.19114;
  124. -0.73801 0.1867 1.2154 0.99643 0.71631 0.99117;
  125. -0.11468 -0.82747 1.5643 1.3337 0.36832 0.45782;
  126. -0.32246 -1.5881 0.51751 2.3454 1.7603 0.1911];
  127. Xmax=max(mg_data);
  128. Xmin=min(mg_data);
  129. e1=ones(124,1);e2=ones(124,6);
  130. %建模数据集m_data[]
  131. m_data=0.9*e2-0.8*(e1*Xmax-mg_data)./(e1*(Xmax-Xmin));