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首页> 外文会议>World Multiconference on Systemics, Cybernetics and Informatics and 5th International Conference on Information Systems Analysis and Synthesis Vol.5: Computer Science and Engineering, Jul 31-Aug 4, 1999, Orlando, Florida >Granular Neuro-fuzzy Knowledge Compression and Expansion
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Granular Neuro-fuzzy Knowledge Compression and Expansion

机译：颗粒神经模糊知识压缩与扩展

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摘要

In order to overcome weaknesses of the conventional crisp neural network and the fuzzy-operation-oriented neural network, we have developed a general fuzzy-reasoning-oriented fuzzy neural network called a Crisp-Fuzzy Neural Network (CFNN) which is capable of extracting high-level knowledge such as fuzzy IF-THEN rules from either crisp data or fuzzy data. A CFNN can effectively compress a 5 x 5 fuzzy IF-THEN rule base of a cart-pole balancing system to a 2 x 2 one, and can also expand an invalid sparse 3 x 3 fuzzy IF-THEN rule base to a valid 5 x 5 one. Therefore, a CFNN is an efficient neuro-fuzzy system with abilities of discovering new fuzzy knowledge from either numerical data or fuzzy data, compressing and expending fuzzy knowledge.
机译：为了克服传统的脆神经网络和面向模糊操作的神经网络的弱点，我们开发了一种通用的面向模糊推理的模糊神经网络，称为Crisp-Fuzzy神经网络（CFNN），它能够提取高清晰数据或模糊数据等模糊IF-THEN规则等高级知识。 CFNN可以有效地将车杆平衡系统的5 x 5模糊IF-THEN规则库压缩为2 x 2一个，也可以将无效的3 x 3模糊IF-THEN规则库扩展为有效的5 x 5个。因此，CFNN是一种有效的神经模糊系统，具有从数值数据或模糊数据中发现新的模糊知识，压缩和扩展模糊知识的能力。

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