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Diterbitkan olehWidya Hermawan Telah diubah "9 tahun yang lalu
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1 Pertemuan 8 JARINGAN COMPETITIVE Matakuliah: H0434/Jaringan Syaraf Tiruan Tahun: 2005 Versi: 1
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2 Learning Outcomes Pada akhir pertemuan ini, diharapkan mahasiswa akan mampu : Menjelaskan konsep Jaringan Competitive
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3 Outline Materi Arsitektur Jaringan Learning Rule
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4 Hamming Network
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5 Layer 1 (Correlation) We want the network to recognize the following prototype vectors: W 1 w T 1 w T 2 w T S p 1 T p 2 T p Q T == b 1 R R R = The first layer weight matrix and bias vector are given by: The response of the first layer is: The prototype closest to the input vector produces the largest response. a 1 W 1 pb 1 + p 1 T p R+ p 2 T p R+ p Q T p R+ ==
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6 Layer 2 (Competition) The neuron with the largest initial condition will win the competiton. The second layer is initialized with the output of the first layer.
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7 Competitive Layer nWp w 1 T w 2 T w S T p w 1 T p w 2 T p w S T p L 2 1 cos L 2 2 L 2 S ====
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8 Competitive Learning For the competitive network, the winning neuron has an ouput of 1, and the other neurons have an output of 0. Instar Rule Kohonen Rule
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9 Graphical Representation
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10 Example
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11 Four Iterations
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12 Typical Convergence (Clustering) Before TrainingAfter Training Weights Input Vectors
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13 Dead Units One problem with competitive learning is that neurons with initial weights far from any input vector may never win. Dead Unit Solution: Add a negative bias to each neuron, and increase the magnitude of the bias as the neuron wins. This will make it harder to win if a neuron has won often. This is called a “conscience.”
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