A RECONFIGURABLE FUZZY NEURAL-NETWORK WITH IN-SITU LEARNING

被引：2
作者：
PEDRYCZ, W
POSKAR, CH
CZEZOWSKI, PJ
机构：
来源：
IEEE MICRO | 1995年 / 15卷 / 04期
基金：
加拿大自然科学与工程研究理事会;
关键词：
D O I：
10.1109/40.400639
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
Our reconfigurable fuzzy processor (RFP) implements both aggregative and referential operations. Its architecture combines structural and parametric flexibility in a network implementing RFPs as a collection of fuzzy neurons. A fuzzy neural network using a bidirectionally Linked series of shared buses facilitates a modular and scalable design environment for the RFP. An appropriate interface, separate from the RFP neuron itself, promotes the reuse of the neuron design with alternative interconnection networks.
引用
收藏
页码：19 / 30
页数：12
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