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Neural network (II) — HNN Hopfield Neural Network. Date : 2002/09/24 Present by John Chen E-mail : phd9008@cs.nchu.edu.tw. Outline. Preliminaries Introduction HNN algorithm Application & Researh Topic Conclusion. Preliminaries.

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neural network ii hnn hopfield neural network

Neural network (II) —HNNHopfield Neural Network

Date : 2002/09/24

Present by John Chen

E-mail : phd9008@cs.nchu.edu.tw

outline
Outline
  • Preliminaries
  • Introduction
  • HNN algorithm
  • Application & Researh Topic
  • Conclusion
preliminaries
Preliminaries
  • Neural Networks are built of neurons and their connections
  • The characteristics of Neural Network
    • Learning capability
    • The capability of Storage
    • Fault tolerance
    • The capability of induce
    • Pallel processing
preliminaries cont
Preliminaries(cont)
  • Hebb learning rule
    • Question : How to learn ? Where to keep memory?
    • Hebb proposed learning rule in 1949
    • Learning is just a local appearance , it is correlated with the excited degrees between connected neurons
    • It is also called correlated learning rule
    • dWij/dt = SjXj
preliminaries cont1
Preliminaries(cont)

Fig Computing Model of Neuron

introduction
Introduction
  • Concept ofHopfield Neural Network
    • Proposed by J. Hopfield in 1982
    • Provide the base of research theory
  • Graph of Hopfield Neural Network
introduction cont
Introduction (cont)
  • The properties of HNN
    • Parallel input , Parallel output
    • Operation process divide into two part
      • Memorizing process
      • Remembering process
    • IN memorizing process
      • Update weights by Hebb learning rule
      • ∆Wij=ηXiXj
    • IN remembring process
      • Output a result most similar to memorizing example by calculating
introduction cont1
Introduction (cont)
  • Two type of HNN
    • Discrete : (1 or -1) , (0 or 1)
    • Continuous : real value between 0,1
  • The cost function of HNN
    • Xi:status value of i’th neuron
    • Xj: status value of j’th neuron
    • Wij:connection weight between i’th & j’th neuron
    • θj:bias value of j’th neuron
hnn algorithm
HNN Algorithm
  • Algorithm of memorizing
    • Step 1 : set network parameters
    • Step 2 : read connection weights

set Wij= XipXjp and Wii=0

hnn algorithm cont
HNN Algorithm(cont)
  • Algorithm of remembering
    • Step 1 : set network parameters
    • Step 2 : read connection weights
    • Step 3 : Input initial vector X
    • Step 4 : Calculate new vector X

neti= WijXj

1 if neti>0

Xinew=Xiold if neti=0

-1 if neti<0

    • Step 5 : repeat until network converge
application researh topic
Application & Researh Topic
  • 時空型霍菲爾類神經網路於鼻咽部復發腫瘤之偵測 — TAAI 2001
    • 作者:張傳育 樹德科技大學 資訊工程系
    • 提出立體時空型霍菲爾類神經網路(SHNC)

來偵測鼻咽部復發腫瘤

    • SHNC 結合動態影像的時空資訊及像素點間的結構資訊,對每個像素點作分類;可有效過濾影像中的雜訊
    • 採用了競爭式的學習法則,加快了網路收斂的速度
    • 霍菲爾類神經網路(HNN)是屬於非監督式學習網路,免除了事先訓練網路的麻煩
    • 經實驗可知SHNC所偵測的結果比 K-means , PCA等方法來得正確有效率
application researh topic cont
Application & Researh Topic(cont)
  • Neural Networks for Visual Cryptography

— with Examples for complex Acess Schemes

    • Author : Suchen Chiang , Tai-Wen Yue Tatung University
    • Proposed Q’tron NN Model
    • Derived from HNN
    • Design Energies function
      • For Halftoning
      • For Restoration
      • For (2,2) Visual Cryptography
    • Can be extend for (2,2) Visual Cryptography but in another paper
application researh topic cont1
Application & Researh Topic(cont)
  • Reseach Reference
    • Journal
      • Neural Network
      • IEEE Trans. on Neural Network
      • IEEE Trans. On system, Man , and Cybernetics
      • IEICE Trans. On Information and system
    • Conference
      • ICNN 1987 ~ 1988 (神經網路國際研討會)
      • IJCNN 1989 ~ (神經網路國際聯合研討會) : IEEE and INNS
      • TAAI 人工智慧與應用研討會(台灣)
application researh topic cont2
Application & Researh Topic(cont)
  • Research Direction
    • Read and Reference
    • Preprocess
    • Model Modify
conclusion
Conclusion
  • Research like the learning rule of

Neural Network

  • If I have more time or more resource