NEURAL NETWORKS FOR INGRESS MONITORING

• US 20090328126A1
• Filed: 09/04/2009
• Published: 12/31/2009
• Est. Priority Date: 07/20/1999
• Status: Abandoned Application

• Current Assignee: Gary W. Sinde
• Original Assignee: Gary W. Sinde
• Inventors: Sinde, Gary W.
• Application Number:
  US12/554,532
• Publication Number:
  US 20090328126A1
• Time in Patent Office: Days
• US Class Current: 725/125
• CPC Class Codes: H04N 17/00 Diagnosis, testing or measuring for television systems or their details

Abstract

A method of identifying a source of ingress into a network includes storing frequency spectra of known sources of ingress, comparing the frequency spectrum of ingress to the frequency spectra of known sources of ingress, and determining from the comparison which of the frequency spectra of known sources of ingress is closest to the frequency spectrum of the ingress. Apparatus for identifying a source of ingress into a network includes memory for storing frequency spectra of known sources of ingress and a device for comparing the frequency spectrum of the ingress to frequency spectra of known sources of ingress and determining from the comparison which frequency spectrum of a known source of ingress is closest to the frequency spectrum of the ingress. A method of establishing ingress into a network includes developing a first frequency spectrum indicative of the condition of the network at a first time during the operation of the network, developing a second frequency spectrum indicative of the condition of the network at a second, later time, comparing the second frequency spectrum to the first frequency spectrum, and determining from the comparison a condition of the network at the second time. Apparatus for establishing ingress into a network includes a device for receiving frequency spectra. The device receives at least one first frequency spectrum indicative of the condition of the network at a first time during the operation of the network, and a second frequency spectrum indicative of the condition of the network at a second, later time. The device compares the second frequency spectrum to the first frequency spectrum and determines from the comparison the condition of the network at the second time.

Claims

1-40. -40. (canceled)

41. A method of establishing ingress into a network including developing a first frequency spectrum indicative of the condition of the network at a first time during the operation of the network, developing a second frequency spectrum indicative of the condition of the network at a second, later time, comparing the second frequency spectrum to the first frequency spectrum, and determining from the comparison a condition of the network at the second time.

42. The method of claim 41 wherein the first frequency spectrum indicative of the condition of the network at a first time during the operation of the network includes a first frequency spectrum indicative of a baseline condition of the network.

43. The method of claim 41 wherein comparing the second frequency spectrum to the first frequency spectrum and determining from the comparison the condition of the network at the second time together include finding an optimum solution to the problem of comparison of the second frequency spectrum to the first frequency spectrum.

44. The method of claim 43 wherein finding an optimum solution to the problem of comparison of the second frequency spectrum to the first frequency spectrum includes teaching a neural network the first frequency spectrum.

45. The method of claim 44 wherein finding an optimum solution to the problem of comparison of the second frequency spectrum to the first frequency spectrum includes using a back propagation neural network to find an optimum solution to the problem of comparison of the second frequency spectrum to the first frequency spectrum.

46. The method of claim 45 wherein teaching a neural network the first frequency spectrum and using a back propagation neural network to find an optimum solution to the problem of comparison of the second frequency spectrum to the first frequency spectrum together include using a particle swarm optimizer to find an optimum solution to the problem of comparison of the second frequency spectrum to the first frequency spectrum.

47. The method of claim 41 further including digitizing the second frequency spectrum.

48. The method of claim 47 wherein comparing the thus-digitized second frequency spectrum to the first frequency spectrum and determining from the comparison the condition of the network at the second time together include finding an optimum solution to the problem of comparison of the thus-digitized second frequency spectrum to the first frequency spectrum.

49. The method of claim 48 wherein finding an optimum solution to the problem of comparison of the thus-digitized second frequency spectrum to the first frequency spectrum includes teaching a neural network the first frequency spectrum.

50. The method of claim 49 wherein finding an optimum solution to the problem of comparison of the thus-digitized second frequency spectrum to the first frequency spectrum includes using a back propagation neural network to find an optimum solution to the problem of comparison of the thus-digitized second frequency spectrum to the first frequency spectrum.

51. The method of claim 50 wherein teaching a neural network the first frequency spectrum and using a back propagation neural network to find an optimum solution to the problem of comparison of the thus-digitized second frequency spectrum to the first frequency spectrum together include using a particle swarm optimizer to find an optimum solution to the problem of comparison of the thus-digitized second frequency spectrum to the first frequency spectrum.

52. The method of claim 47 wherein comparing the second frequency spectrum to the first frequency spectrum includes digitizing the first frequency spectrum.

53. The method of claim 52 wherein comparing the thus-digitized second frequency spectrum to the thus-digitized first frequency spectrum and determining from the comparison a condition of the network together include finding an optimum solution to the problem of comparison of the thus-digitized second frequency spectrum to the thus-digitized first frequency spectrum.

54. The method of claim 53 wherein finding an optimum solution to the problem of comparison of the thus-digitized second frequency spectrum to the thus-digitized first frequency spectrum includes teaching a neural network the thus-digitized first frequency spectrum.

55. The method of claim 54 wherein finding an optimum solution to the problem of comparison of the thus-digitized second frequency spectrum to the thus-digitized first frequency spectrum includes using a back propagation neural network to find an optimum solution to the problem of comparison of the thus-digitized second frequency spectrum to the thus-digitized first frequency spectrum.

56. The method of claim 55 wherein teaching a neural network the thus-digitized first frequency spectrum and using a back propagation neural network to find an optimum solution to the problem of comparison of the thus-digitized second frequency spectrum to the thus-digitized first frequency spectrum together include using a particle swarm optimizer to find an optimum solution to the problem of comparison of the thus-digitized second frequency spectrum to the thus-digitized first frequency spectrum.

57. The method of claim 41 wherein comparing the second frequency spectrum to the first frequency spectrum includes digitizing the first frequency spectrum.

58. The method of claim 57 wherein comparing the second frequency spectrum to the thus-digitized first frequency spectrum and determining from the comparison a condition of the network together include finding an optimum solution to the problem of comparison of the second frequency spectrum to the thus-digitized first frequency spectrum.

59. The method of claim 58 wherein finding an optimum solution to the problem of comparison of the second frequency spectrum to the thus-digitized first frequency spectrum includes teaching a neural network the thus-digitized first frequency spectrum.

60. The method of claim 59 wherein finding an optimum solution to the problem of comparison of the second frequency spectrum to the thus-digitized first frequency spectrum includes using a back propagation neural network to find an optimum solution to the problem of comparison of the second frequency spectrum to the thus-digitized first frequency spectrum.

61. The method of claim 60 wherein teaching a neural network the thus-digitized first frequency spectrum and using a back propagation neural network to find an optimum solution to the problem of comparison of the second frequency spectrum to the thus-digitized first frequency spectrum together include using a particle swarm optimizer to find an optimum solution to the problem of comparison of the second frequency spectrum to the thus-digitized first frequency spectrum.

62. The method of claim 41 wherein the at least one first frequency spectrum indicative of the condition of the network at a first time during the operation of the network includes multiple first frequency spectra indicative of the condition of the network at multiple first times during the operation of the network, and the multiple first frequency spectra are combined prior to comparing the second frequency spectrum to the combined first frequency spectra.

63. The method of claim 62 wherein the combined first frequency spectra indicative of the condition of the network at multiple first times during the operation of the network include combined first frequency spectra indicative of a baseline condition of the network.

64. The method of claim 62 wherein comparing the second frequency spectrum to the combined first frequency spectra and determining from the comparison the condition of the network at the second time together include finding an optimum solution to the problem of comparison of the second frequency spectrum to the combined first frequency spectra.

65. The method of claim 64 wherein finding an optimum solution to the problem of comparison of the second frequency spectrum to the combined first frequency spectra includes teaching a neural network the combined first frequency spectra.

66. The method of claim 65 wherein finding an optimum solution to the problem of comparison of the second frequency spectrum to the combined first frequency spectra includes using a back propagation neural network to find an optimum solution to the problem of comparison of the second frequency spectrum to the combined first frequency spectra.

67. The method of claim 66 wherein teaching a neural network the combined first frequency spectra and using a back propagation neural network to find an optimum solution to the problem of comparison of the second frequency spectrum to the combined first frequency spectra together include using a particle swarm optimizer to find an optimum solution to the problem of comparison of the second frequency spectrum to the combined first frequency spectra.

68. The method of claim 62 further including digitizing the second frequency spectrum.

69. The method of claim 68 wherein comparing the thus-digitized second frequency spectrum to the combined first frequency spectra and determining from the comparison the condition of the network at the second time together include finding an optimum solution to the problem of comparison of the thus-digitized second frequency spectrum to the combined first frequency spectra.

70. The method of claim 69 wherein finding an optimum solution to the problem of comparison of the thus-digitized second frequency spectrum to the combined first frequency spectra includes teaching a neural network the combined first frequency spectra.

71. The method of claim 70 wherein finding an optimum solution to the problem of comparison of the thus-digitized second frequency spectrum to the combined first frequency spectra includes using a back propagation neural network to find an optimum solution to the problem of comparison of the thus-digitized second frequency spectrum to the combined first frequency spectra.

72. The method of claim 71 wherein teaching a neural network the combined first frequency spectra and using a back propagation neural network to find an optimum solution to the problem of comparison of the thus-digitized second frequency spectrum to the combined first frequency spectra together include using a particle swarm optimizer to find an optimum solution to the problem of comparison of the thus-digitized second frequency spectrum to the combined first frequency spectra.

73. The method of claim 68 wherein comparing the second frequency spectrum to the combined first frequency spectra includes digitizing the first frequency spectra.

74. The method of claim 73 wherein comparing the thus-digitized second frequency spectrum to the thus-digitized combined first frequency spectra and determining from the comparison a condition of the network together include finding an optimum solution to the problem of comparison of the thus-digitized second frequency spectrum to the thus-digitized combined first frequency spectra.

75. The method of claim 74 wherein finding an optimum solution to the problem of comparison of the thus-digitized second frequency spectrum to the thus-digitized combined first frequency spectra includes teaching a neural network the thus-digitized combined first frequency spectra.

76. The method of claim 75 wherein finding an optimum solution to the problem of comparison of the thus-digitized second frequency spectrum to the thus-digitized combined first frequency spectra includes using a back propagation neural network to find an optimum solution to the problem of comparison of the thus-digitized second frequency spectrum to the thus-digitized combined first frequency spectra.

77. The method of claim 76 wherein teaching a neural network the thus-digitized combined first frequency spectra and using a back propagation neural network to find an optimum solution to the problem of comparison of the thus-digitized second frequency spectrum to the thus-digitized combined first frequency spectra together include using a particle swarm optimizer to find an optimum solution to the problem of comparison of the thus-digitized second frequency spectrum to the thus-digitized combined first frequency spectra.

78. The method of claim 62 wherein comparing the second frequency spectrum to the combined first frequency spectra includes digitizing the combined first frequency spectra.

79. The method of claim 78 wherein comparing the second frequency spectrum to the thus-digitized combined first frequency spectra and determining from the comparison a condition of the network together include finding an optimum solution to the problem of comparison of the second frequency spectrum to the thus-digitized combined first frequency spectra.

80. The method of claim 79 wherein finding an optimum solution to the problem of comparison of the second frequency spectrum to the thus-digitized combined first frequency spectra includes teaching a neural network the thus-digitized combined first frequency spectra.

81. The method of claim 80 wherein finding an optimum solution to the problem of comparison of the second frequency spectrum to the thus-digitized combined first frequency spectra includes using a back propagation neural network to find an optimum solution to the problem of comparison of the second frequency spectrum to the thus-digitized combined first frequency spectra.

82. The method of claim 81 wherein teaching a neural network the thus-digitized combined first frequency spectra and using a back propagation neural network to find an optimum solution to the problem of comparison of the second frequency spectrum to the thus-digitized combined first frequency spectra together include using a particle swarm optimizer to find an optimum solution to the problem of comparison of the second frequency spectrum to the thus-digitized combined first frequency spectra.

83. Apparatus for establishing ingress into a network, the apparatus including a device for receiving frequency spectra, the device receiving at least one first frequency spectrum indicative of the condition of the network at a first time during the operation of the network, the device receiving a second frequency spectrum indicative of the condition of the network at a second, later time, the device further comparing the second frequency spectrum to the first frequency spectrum and determining from the comparison the condition of the network at the second time.

84. The apparatus of claim 83 wherein the first frequency spectrum indicative of the condition of the network at a first time during the operation of the network includes a first frequency spectrum indicative of a baseline condition of the network.

85. The apparatus of claim 83 wherein the device for comparing the second frequency spectrum to the first frequency spectrum and determining from the comparison the condition of the network at the second time together include a device for finding an optimum solution to the problem of comparison of the second frequency spectrum to the first frequency spectrum.

86. The apparatus of claim 85 wherein the device for finding an optimum solution to the problem of comparison of the second frequency spectrum to the first frequency spectrum includes a device for teaching a neural network the first frequency spectrum.

87. The apparatus of claim 86 wherein the device for finding an optimum solution to the problem of comparison of the second frequency spectrum to the first frequency spectrum includes a device for using a back propagation neural network to find an optimum solution to the problem of comparison of the second frequency spectrum to the first frequency spectrum.

88. The apparatus of claim 87 wherein the device for teaching a neural network the first frequency spectrum and using a back propagation neural network to find an optimum solution to the problem of comparison of the second frequency spectrum to the first frequency spectrum together include a device for using a particle swarm optimizer to find an optimum solution to the problem of comparison of the second frequency spectrum to the first frequency spectrum.

89. The apparatus of claim 83 wherein the device further includes a device for digitizing the second frequency spectrum.

90. The apparatus of claim 89 wherein the device for comparing the thus-digitized second frequency spectrum to the first frequency spectrum and determining from the comparison the condition of the network at the second time together include a device for finding an optimum solution to the problem of comparison of the thus-digitized second frequency spectrum to the first frequency spectrum.

91. The apparatus of claim 90 wherein the device for finding an optimum solution to the problem of comparison of the thus-digitized second frequency spectrum to the first frequency spectrum includes a device for teaching a neural network the first frequency spectrum.

92. The apparatus of claim 91 wherein the device for finding an optimum solution to the problem of comparison of the thus-digitized second frequency spectrum to the first frequency spectrum includes a device for using a back propagation neural network to find an optimum solution to the problem of comparison of the thus-digitized second frequency spectrum to the first frequency spectrum.

93. The apparatus of claim 92 wherein the device for teaching a neural network the first frequency spectrum and using a back propagation neural network to find an optimum solution to the problem of comparison of the thus-digitized second frequency spectrum to the first frequency spectrum together include a device for using a particle swarm optimizer to find an optimum solution to the problem of comparison of the thus-digitized second frequency spectrum to the first frequency spectrum.

94. The apparatus of claim 89 wherein the device for comparing the second frequency spectrum to the first frequency spectrum includes digitizing the first frequency spectrum.

95. The apparatus of claim 94 wherein the device for comparing the thus-digitized second frequency spectrum to the thus-digitized first frequency spectrum and determining from the comparison a condition of the network together include a device for finding an optimum solution to the problem of comparison of the thus-digitized second frequency spectrum to the thus-digitized first frequency spectrum.

96. The apparatus of claim 95 wherein the device for finding an optimum solution to the problem of comparison of the thus-digitized second frequency spectrum to the thus-digitized first frequency spectrum includes a device for teaching a neural network the thus-digitized first frequency spectrum.

97. The apparatus of claim 96 wherein the device for finding an optimum solution to the problem of comparison of the thus-digitized second frequency spectrum to the thus-digitized first frequency spectrum includes a device for using a back propagation neural network to find an optimum solution to the problem of comparison of the thus-digitized second frequency spectrum to the thus-digitized first frequency spectrum.

98. The apparatus of claim 97 wherein the device for teaching a neural network the thus-digitized first frequency spectrum and using a back propagation neural network to find an optimum solution to the problem of comparison of the thus-digitized second frequency spectrum to the thus-digitized first frequency spectrum together include a device for using a particle swarm optimizer to find an optimum solution to the problem of comparison of the thus-digitized second frequency spectrum to the thus-digitized first frequency spectrum.

99. The apparatus of claim 83 wherein the device for comparing the second frequency spectrum to the first frequency spectrum includes a device for digitizing the first frequency spectrum.

100. The apparatus of claim 99 wherein the device for comparing the second frequency spectrum to the thus-digitized first frequency spectrum and determining from the comparison a condition of the network together include a device for finding an optimum solution to the problem of comparison of the second frequency spectrum to the thus-digitized first frequency spectrum.

101. The apparatus of claim 100 wherein the device for finding an optimum solution to the problem of comparison of the second frequency spectrum to the thus-digitized first frequency spectrum includes a device for teaching a neural network the thus-digitized first frequency spectrum.

102. The apparatus of claim 101 wherein the device for finding an optimum solution to the problem of comparison of the second frequency spectrum to the thus-digitized first frequency spectrum includes a device for using a back propagation neural network to find an optimum solution to the problem of comparison of the second frequency spectrum to the thus-digitized first frequency spectrum.

103. The apparatus of claim 102 wherein the device for teaching a neural network the thus-digitized first frequency spectrum and using a back propagation neural network to find an optimum solution to the problem of comparison of the second frequency spectrum to the thus-digitized first frequency spectrum together include a device for using a particle swarm optimizer to find an optimum solution to the problem of comparison of the second frequency spectrum to the thus-digitized first frequency spectrum.

104. The apparatus of claim 83 wherein the at least one first frequency spectrum indicative of the condition of the network at a first time during the operation of the network includes multiple first frequency spectra indicative of the condition of the network at multiple first times during the operation of the network, and the device combines the multiple first frequency spectra prior to comparing the second frequency spectrum to the combined first frequency spectra.

105. The apparatus of claim 104 wherein the combined first frequency spectra indicative of the condition of the network at multiple first times during the operation of the network include combined first frequency spectra indicative of a baseline condition of the network.

106. The apparatus of claim 104 wherein the device for comparing the second frequency spectrum to the combined first frequency spectra and determining from the comparison the condition of the network at the second time together include a device for finding an optimum solution to the problem of comparison of the second frequency spectrum to the combined first frequency spectra.

107. The apparatus of claim 106 wherein the device for finding an optimum solution to the problem of comparison of the second frequency spectrum to the combined first frequency spectra includes a device for teaching a neural network the combined first frequency spectra.

108. The method of claim 107 wherein the device for finding an optimum solution to the problem of comparison of the second frequency spectrum to the combined first frequency spectra includes a device for using a back propagation neural network to find an optimum solution to the problem of comparison of the second frequency spectrum to the combined first frequency spectra.

109. The apparatus of claim 108 wherein the device for teaching a neural network the combined first frequency spectra and using a back propagation neural network to find an optimum solution to the problem of comparison of the second frequency spectrum to the combined first frequency spectra together include a device for using a particle swarm optimizer to find an optimum solution to the problem of comparison of the second frequency spectrum to the combined first frequency spectra.

110. The apparatus of claim 104 wherein the device further includes a device for digitizing the second frequency spectrum.

111. The apparatus of claim 110 wherein the device for comparing the thus-digitized second frequency spectrum to the combined first frequency spectra and determining from the comparison the condition of the network at the second time together include a device for finding an optimum solution to the problem of comparison of the thus-digitized second frequency spectrum to the combined first frequency spectra.

112. The apparatus of claim 111 wherein the device for finding an optimum solution to the problem of comparison of the thus-digitized second frequency spectrum to the combined first frequency spectra includes a device for teaching a neural network the combined first frequency spectra.

113. The apparatus of claim 112 wherein the device for finding an optimum solution to the problem of comparison of the thus-digitized second frequency spectrum to the combined first frequency spectra includes a device for using a back propagation neural network to find an optimum solution to the problem of comparison of the thus-digitized second frequency spectrum to the combined first frequency spectra.

114. The apparatus of claim 113 wherein the device for teaching a neural network the combined first frequency spectra and using a back propagation neural network to find an optimum solution to the problem of comparison of the thus-digitized second frequency spectrum to the combined first frequency spectra together include a device for using a particle swarm optimizer to find an optimum solution to the problem of comparison of the thus-digitized second frequency spectrum to the combined first frequency spectra.

115. The apparatus of claim 110 wherein the device for comparing the second frequency spectrum to the combined first frequency spectra includes a device for digitizing the first frequency spectra.

116. The apparatus of claim 115 wherein the device for comparing the thus-digitized second frequency spectrum to the thus-digitized combined first frequency spectra and determining from the comparison a condition of the network together include a device for finding an optimum solution to the problem of comparison of the thus-digitized second frequency spectrum to the thus-digitized combined first frequency spectra.

117. The apparatus of claim 116 wherein the device for finding an optimum solution to the problem of comparison of the thus-digitized second frequency spectrum to the thus-digitized combined first frequency spectra includes a device for teaching a neural network the thus-digitized combined first frequency spectra.

118. The apparatus of claim 117 wherein the device for finding an optimum solution to the problem of comparison of the thus-digitized second frequency spectrum to the thus-digitized combined first frequency spectra includes a device for using a back propagation neural network to find an optimum solution to the problem of comparison of the thus-digitized second frequency spectrum to the thus-digitized combined first frequency spectra.

119. The apparatus of claim 118 wherein the device for teaching a neural network the thus-digitized combined first frequency spectra and using a back propagation neural network to find an optimum solution to the problem of comparison of the thus-digitized second frequency spectrum to the thus-digitized combined first frequency spectra together include a device for using a particle swarm optimizer to find an optimum solution to the problem of comparison of the thus-digitized second frequency spectrum to the thus-digitized combined first frequency spectra.

120. The apparatus of claim 104 wherein the device for comparing the second frequency spectrum to the combined first frequency spectra includes a device for digitizing the combined first frequency spectra.

121. The apparatus of claim 120 wherein the device for comparing the second frequency spectrum to the thus-digitized combined first frequency spectra and determining from the comparison a condition of the network together include a device for finding an optimum solution to the problem of comparison of the second frequency spectrum to the thus-digitized combined first frequency spectra.

122. The apparatus of claim 121 wherein the device for finding an optimum solution to the problem of comparison of the second frequency spectrum to the thus-digitized combined first frequency spectra includes a device for teaching a neural network the thus-digitized combined first frequency spectra.

123. The apparatus of claim 122 wherein the device for finding an optimum solution to the problem of comparison of the second frequency spectrum to the thus-digitized combined first frequency spectra includes a device for using a back propagation neural network to find an optimum solution to the problem of comparison of the second frequency spectrum to the thus-digitized combined first frequency spectra.

124. The apparatus of claim 123 wherein the device for teaching a neural network the thus-digitized combined first frequency spectra and using a back propagation neural network to find an optimum solution to the problem of comparison of the second frequency spectrum to the thus-digitized combined first frequency spectra together include a device for using a particle swarm optimizer to find an optimum solution to the problem of comparison of the second frequency spectrum to the thus-digitized combined first frequency spectra.