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Neural network (II) — HNN Hopfield Neural NetworkPowerPoint Presentation

Neural network (II) — HNN Hopfield Neural Network

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### Neural network (II) —HNNHopfield Neural Network

Date : 2002/09/24

Present by John Chen

E-mail : [email protected]

Outline

- Preliminaries
- Introduction
- HNN algorithm
- Application & Researh Topic
- Conclusion

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)

- 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(cont)

Fig Computing Model of Neuron

Introduction

- Concept ofHopfield Neural Network
- Proposed by J. Hopfield in 1982
- Provide the base of research theory

- Graph of Hopfield Neural Network

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 (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

- Algorithm of memorizing
- Step 1 : set network parameters
- Step 2 : read connection weights
set Wij＝ XipXjp and Wii＝0

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

- 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)

- 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(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)

- Journal

Application & Researh Topic(cont)

- Research Direction
- Read and Reference
- Preprocess
- Model Modify

Conclusion

- Research like the learning rule of
Neural Network

- If I have more time or more resource

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