Neural network ii hnn hopfield neural network
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Neural network (II) — HNN Hopfield Neural Network. Date : 2002/09/24 Present by John Chen E-mail : [email protected] Outline. Preliminaries Introduction HNN algorithm Application & Researh Topic Conclusion. Preliminaries.

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Neural network (II) — HNN Hopfield Neural Network

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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 : [email protected]


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


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