大部的類神經網路均為解決特定問題而設計，並非真正去模擬人腦的功能
，在本論文中介紹一個模擬人類學習方式的類神經網路，稱為推理類神經
網路（The Reasoning Neural Network），其主要兩個組成為強記（
cram -ming）及推理（reasoning）部份，透過彈性的組合這兩個部份可
使類神經網路具有類似人類的學習程序。在本論文中介紹其中一個學習程
序並用四個實驗來評估推理類神經網路的績效，從實結果得知，推理類神
經網路能以合理的隱藏節點數（hidden nodes）達到學習的目標，並建立
一個網路內部表示方式（internal representation），及具有好的推理
能力（g eneralization ability）。 / Most of artification Neural Networks are designed to resolve
spe -cific problems, rather than to model the brain. The
Reasoning N -eural Network (RNN) that imitates the way of human
learning is presented here. Two key components of RNN are the
cramming and t -he reasoning. These components coulds be
arranged flexibly to a -chieve the human-like learning
procedure. One edition of the RNN used in experiments is
introduces, and four different proble -ms are used to evaluate
the RNN's performance. From simulation results, the RNN
accomplishes the goal of learning with a reason -able number of
hidden nodes, and evolves a good internal repres -entation and
a generalization ability.
| Identifer | oai:union.ndltd.org:CHENGCHI/B2002003876 |
| Creators | 徐志鈞, Hsu Chih Chun |
| Publisher | 國立政治大學 |
| Source Sets | National Chengchi University Libraries |
| Language | 英文 |
| Detected Language | English |
| Type | text |
| Rights | Copyright © nccu library on behalf of the copyright holders |
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