منابع مشابه
Fuzzy inference and rule extraction using a neural network
This paper proposes a neural network for building and optimizing fuzzy models. The network can be regarded both as an adaptive fuzzy inference system with the capability of learning fuzzy rules from data, and as a connectionist architecture provided with linguistic meaning. Fuzzy rules are extracted from training examples by a hybrid learning scheme comprised of two phases: rule generation phas...
متن کاملFuzzy Association Rule Reduction Using Clustering In Som Neural Network
The major drawback of fuzzy data mining is that after applying fuzzy data mining on the quantitative data, the number of extracted fuzzy association rules is very huge. When many association rules are obtained, the usefulness of them will be reduced. In this paper, we introduce an approach to reduce and summarize the extracted fuzzy association rules after fuzzy data mining. In our approach, in...
متن کاملStructured Logic Processors Based Self Learning Fuzzy Neural Network
-This paper presents a new and simple fuzzy neural network based on structured fuzzy logic processors. The modifications of the referential fuzzy logic processors provide the equality index. The major advantage of the proposed network is that the learning process is faster and it has the capability of self -determining the relative cardinality. The fast learning is due to incorporating the rece...
متن کاملA Trellis-Structured Neural Network
We have developed a neural network which consists of cooperatively interconnected Grossberg on-center off-surround subnets and which can be used to optimize a function related to the log likelihood function for decoding convolutional codes or more general FIR signal deconvolution problems. Connections in the network are confined to neighboring subnets, and it is representative of the types of n...
متن کاملA fuzzy neural network for rule acquiring on fuzzy control systems*
This paper presents a layer-structured fuzzy neural network (FNN) for learning rules of fuzzy-logic control systems. Initially, FNN is constructed to contain all the possible fuzzy rules. We propose a two-phase learning procedure for this network. The first phase is a error-backprop (EBP) training, and the second phase is a rule pruning. Since some functions of the nodes in the FNN have the com...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Japan Society for Fuzzy Theory and Systems
سال: 1992
ISSN: 0915-647X,2432-9932
DOI: 10.3156/jfuzzy.4.5_985