Fuzzy neural logic network and its learning algorithms
The paper introduces the basic features of fuzzy neural logic network. Each fuzzy neural logic network model is trained from a set of knowledge in the form of examples using one of the three learning algorithms introduced. These three learning algorithms are the delta rule controlled learning algori...
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| Main Authors: | , |
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| Format: | text |
| Language: | English |
| Published: |
Institutional Knowledge at Singapore Management University
1991
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| Subjects: | |
| Online Access: | https://ink.library.smu.edu.sg/sis_research/10064 https://ink.library.smu.edu.sg/context/sis_research/article/11064/viewcontent/Fuzzy_neural_logic_network_and_its_learning_algorithms_pv.pdf |
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| Institution: | Singapore Management University |
| Language: | English |
| Summary: | The paper introduces the basic features of fuzzy neural logic network. Each fuzzy neural logic network model is trained from a set of knowledge in the form of examples using one of the three learning algorithms introduced. These three learning algorithms are the delta rule controlled learning algorithm and two mathematical construction algorithms, namely, the local learning method and the global learning method. Once the fuzzy neural logic network model is constructed, it is ready to accept any unknown input from the user. With a low percentage of mismatched features, output solution can be obtained. |
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