| Summary: | 碩士 === 國立交通大學 === 資訊科學學系 === 82 === 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.
|
|---|
