Title: 具有處理模糊語言能力之類神經網路系統
A Neural Fuzzy System with Linguistic Teaching Signals
Authors: 呂雅菁
Ya-Ching Lu
林進燈
Dr. Chin-Teng Lin
資訊科學與工程研究所
Keywords: 模糊集合; 類神經網路; 評論性學習; 倒傳遞法則; 語言性資料;知識庫壓縮;Fuzzy Set; Neural Network; Reinforcement learning; inguistic Data
Issue Date: 1993
Abstract: 本篇論文提出一具有處理模糊語言之類神經網路系統。我們使用α-
level sets形式的模糊集合(fuzzy number)來表示一個語言性質的資料
(linguistic data)。本論文首先提出一個五層的類神經網路架構,這個
架構能處理數值性(numerical)的輸入以及模糊語言的輸入,而且針對不
同性質的應用,產生所需要的數值性的輸出或模糊的輸出。這個架構並不
需要太多的空間及記算時間,而能夠表示任何形態的模糊集合。至於學習
方式,我們討論了有指導性 (supervised) 的學習方式及評論性質
(reinforcement learning) 的學習方式。對於有指導性的學習方式,一
個重要的應用–知識庫壓縮 (rule base concentration) 被展示。至於
評論性質的學習方式,我們強調評論訊息 (reinforcement signal) 是一
種模糊資料,而不像傳統的方式只使用數字來表示。而且針對兩種不同性
質的問題,single-step及 multi- step 的預測方法分別地被提出。常見
的倒單擺 (cart-pole balancing)問題被實際地模擬來顯示我們所提之系
統的功效。
A neural fuzzy system with linguistic teaching signals is
proposed in this thesis. We use fuzzy numbers based on α-level
sets to represent linguistic information. At first, we propose
a five-layered neural network which can process numerical
information as well as linguistic information. Moreover, the
inputs and outputs of this five-layered connectionist
architecture can be a hybrid of fuzzy numbers an numerical
numbers. The important characteristics of the proposed model
are that the network weights can be fuzzy numbers of any shaped
and the performance of this model is superior to several other
methods both in learning speed and memory requirement. Two
kinds of learning schemes are discussed: supervised learning
and reinforcement learning. With supervised learning, the
proposed model can be used for rule base concentration to
reduce the number of rules in knowledge base. For reinforcement
learning, we consider that the reinforcement signal from
environment is linguistic information such as "good" , "very
good", or "bad". We discuss two kinds of reinforcement learning
learning problems:single-step prediction problems and multi-
step prediction problems. Simulation results of the cart- pole
balancing problem are presented to illustrate the performance
and applicability of the proposed reinforcement system.
URI: http://140.113.39.130/cdrfb3/record/nctu/#NT820394039
http://hdl.handle.net/11536/57938
Appears in Collections:Thesis