Pattern Recognition using Neural and Functional Networks 1st Edition by Vasantha Kalyani David, S Rajasekaran ISBN 9783540851295 by Vasantha Kalyani David, Sundaramoorthy Rajasekaran (auth.) 9783540851295, 3540851291 instant download after payment.

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ISBN 13: 9783540851295
Author: Vasantha Kalyani David, S Rajasekaran

Biologically inspiredcomputing isdi?erentfromconventionalcomputing.Ithas adi?erentfeel; often the terminology does notsound like it’stalkingabout machines.The activities ofthiscomputingsoundmorehumanthanmechanistic as peoplespeak ofmachines that behave, react, self-organize,learn, generalize, remember andeven to forget.Much ofthistechnology tries to mimic nature’s approach in orderto mimicsome of nature’s capabilities.They havearigorous, mathematical basisand neuralnetworks forexamplehaveastatistically valid set on which the network istrained. Twooutlinesaresuggestedasthepossibletracksforpatternrecognition.They are neuralnetworks andfunctionalnetworks.NeuralNetworks (many interc- nected elements operating in parallel) carryout tasks that are not only beyond the scope ofconventionalprocessing but also cannotbeunderstood in the same terms.Imagingapplicationsfor neuralnetworksseemtobea natural?t.Neural networks loveto do pattern recognition. A new approachto pattern recognition usingmicroARTMAP together with wavelet transforms in the context ofhand written characters,gestures andsignatures havebeen dealt.The KohonenN- work,Back Propagation Networks andCompetitive Hop?eld NeuralNetwork havebeen considered for various applications. Functionalnetworks,beingageneralizedformofNeuralNetworkswherefu- tionsarelearnedratherthanweightsiscomparedwithMultipleRegressionAn- ysisforsome applicationsandtheresults are seen to be coincident. New kinds of intelligence can be added to machines, and we will havethe possibilityof learningmore about learning.Thus our imaginationsand options are beingstretched.These new machines will be fault-tolerant,intelligentand self-programmingthustryingtomakethemachinessmarter.Soastomakethose who use the techniques even smarter. Chapter1isabrief introduction toNeural and Functionalnetworks in the context of Patternrecognitionusing these disciplinesChapter2 givesa review ofthearchitectures relevantto the investigation andthedevelopment ofthese technologies in the past few decades. Retracted VIII Preface Chapter3begins with the lookattherecognition ofhandwritten alphabets usingthealgorithm for ordered list ofboundary pixelsas well as the Ko- nenSelf-Organizing Map (SOM).Chapter 4 describes the architecture ofthe MicroARTMAP and its capability.

Pattern Recognition using Neural and Functional Networks 1st Table of contents:

1. Introduction
2. Introduction
3. Recognition through Algorithm and Kohonen’s Self Organizing Map
4. MicroARTMAP [35]
5. Wavelet Transforms
6. Gesture Recognition
7. Competitive Hopfield Neural Network
8. Neural and Functional Networks [22]
9. Objectives and Scope of the Investigation
10. Organization of the Book
11. Summary
12. Review of Architectures Relevant to the Investigation
13. Introduction
14. Recognition through Self Organizing Map
15. The $mu$ARTMAP [35]
16. Wavelet Transforms and MicroARTMAP
17. MicroARTMAP and Gesture Recognition
18. Competitive Hopfield Neural Network
19. Functional Networks and Multiple Regression Analysis
20. Summary
21. Recognition of English and Tamil Alphabets Using Kohonen’s Self-organizing Map
22. Introduction
23. Recognition of Handwritten Characters Using Ordered List of Image Pixels on Its Boundary
24. The Kohonen Feature Map
25. Normalization of a Vector
26. Training Law
27. Neighbourhood Size
28. The Kohonen Network
29. Representation of Characters
30. Weight Vector
31. Summary
32. Adaptive Resonance Theory Networks
33. Introduction
34. ART Network
35. Resonant State
36. The STM and LTM Traces
37. The Structure of the ART Model
38. Pattern-Matching Cycle in an ART Network
39. The 2/3 Rule
40. Gain ControlMechanism
41. Fuzzy ART
42. Analogy between ART1 and Fuzzy ART
43. Fast-Learn, Slow-Recode and Complement Coding
44. Complement Coding
45. Weight Vectors
46. Parameters
47. Category Choice
48. Resonance or Reset
49. Learning Law
50. Normalization of Fuzzy ART Inputs
51. Geometric Interpretation of Fuzzy ART
52. Fuzzy ART Category Boxes in Fuzzy Cubes
53. Fuzzy ART Stable Category Learning
54. Fuzzy ARTMAP
55. Fuzzy ARTMAP and MicroARTMAP
56. MicroARTMAP Algorithm (Supervised Neural Network Architecture)
57. Map Field Activation
58. Match Tracking
59. Map Field Learning
60. Defining H
61. Training of $mu$ARTMAP
62. Inter ART Reset
63. Offline Evaluation
64. $mu$ARTMAP Prediction
65. Fast Learning in $mu$ARTMAP
66. Refining a Hyper Box
67. $mu$ARTMAP Rules
68. Summary
69. Applications of MicroARTMAP
70. Introduction
71. Recognition of Handwritten Alphabets by $mu$ARTMAP
72. Recognition of HandwrittenWords by $mu$ARTMAP
73. Recognition of Handwritten Alphabets by $mu$ARTMAP Augmented with Moment-Based Feature Extractor
74. Introduction
75. Steps Involved in Obtaining Moment Invariants
76. Recognition of Handwritten Numbers by $mu$ARTMAP Using Hamming Distance
77. Recognition of Alphabets and Numbers Using $mu$ARTMAP with Only One Exemplar for Training
78. Recognition of Alphabets by $mu$ARTMAP with Increased Sample Size
79. BIS Classification of Soil
80. Plastification of Clamped Isotropic Plate
81. Application to Earthquake Engineering
82. Summary
83. Wavelet Transforms and MicroARTMAP
84. Introduction
85. The Need for Transforms
86. Fourier Transform
87. Transforms Available
88. Wavelet Transforms
89. Continuous Wavelet Transforms (CWT)
90. DiscreteWavelet Transforms (DWT)
91. Wavelet Functions
92. Wavelet Analysis
93. Schematic Representation of the Working of a Wavelet
94. Handwritten Characters Recognition UsingWavelet Transforms and MicroARTMAP
95. Wavelet Transforms in Two Dimensions
96. The Two-Dimensional DWT
97. Recognition of Handwritten Alphabets UsingWaveletPackets and MicroARTMAP
98. The Decomposition Space Tree
99. Analysis Tree
100. Finding Optimal Decomposition
101. Efficient Algorithm for Minimal Entropy Solutions
102. Denoising Using MATLAB for Handwritten Characters
103. MicroARTMAP andWavelet Packets
104. Summary
105. Gesture and Signature Recognition Using MicroARTMAP
106. Introduction
107. Gestures
108. Gesture Recognition
109. Voice Recognition
110. HandWriting Recognition
111. Hand Gestures in HCI (Human – Computer Interaction)
112. Gesture Processing
113. Gesture Acquisition – Requirements
114. Gesture Preprocessing
115. Feature Extraction
116. Statistical Approach
117. Block Processing
118. Wavelet Approach
119. DWT Selection
120. Neural Network for Gesture Recognition
121. Application- Robotics – Robotic Arm Model
122. Interface Circuit
123. Back Propagation Network
124. Statistical Approach
125. Block Processing: For 16 Features
126. Wavelet Approach
127. MicroARTMAP
128. Signature Recognition Using MicroARTMAP and Block Processing
129. Summary
130. Solving Scheduling Problems with Competitive Hopfield Neural Networks
131. Introduction
132. The Energy Function
133. Algorithm
134. Simulation Example Case (i)
135. Example Case (ii)
136. Summary
137. Functional Networks
138. Introduction
139. Functional Networks
140. Procedure to Work with Functional Networks
141. The Associativity Functional Network
142. Multiple Regression Methods and Functional Networks
143. Rock Identification by Functional Networks
144. Hot Extrusion of Steel
145. Summary
146. Conclusions and Suggestions for Future Work
147. Conclusions
148. Suggestions for Future Work
149. References

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