Fuzzy Neural-Logic Networks

Chan, Shih Huang; Hsu, Ling-Yuan; Loe, Kia-Fock; Teh, H. H.; Goh, T.H.

doi:10.1142/9789814503679_0005

Cited by 4 publications

(3 citation statements)

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“…NEULONET was first introduced by Chan et al (1993a, 1993bQiang 1992). A typical NEULONET contains a set of input nodes and output nodes ( Figure 1).…”

Section: Neural Logic Network -Neulonetmentioning

confidence: 99%

“…In this section, we will give a brief description of the Neural Logic Network (NEULONET) and some references for readers who are interested to know more about this particular neural network model. NEULONET was first introduced by Chan et al (1993a, 1993bQiang 1992). A typical NEULONET contains a set of input nodes and output nodes ( Figure 1).…”

Section: Neural Logic Network -Neulonetmentioning

confidence: 99%

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A shell environment for developing connectionist decision support systems*

et al. 1994

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This paper presents the architecture of a neural network expert system shell. The system captures every rule as a rudimentary neural network, which is calleda network element (netel). The aim is topreserve the semantic struciure of the expert system rules, while incorporating the learning capability of neural networks into the inferencing mechanism. These netel rules are dynamically linked up to form the rule-tree during the inferencingprocess, just as a conventional expert system does. The system is also able to adjust its inference strategy according to diferent users and situations. A rule editor is provided to enable easy maintenance of the netel rules. These components are housed under a user-Fiendly interface. An application * This project is sponsored by the National Universily o/Singapore research grant RP900628.Please address aN correspondence to Tong-Seng Quah, quahtsaiss. nus.sg. expert system for USficture bonds trading is built upon this shell. The connectionist expert system has demonstrated its strength over the conventional rule-based system.

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“…NEULONET was first introduced by Chan et al (1993a, 1993bQiang 1992). A typical NEULONET contains a set of input nodes and output nodes ( Figure 1).…”

Section: Neural Logic Network -Neulonetmentioning

confidence: 99%

Section: Neural Logic Network -Neulonetmentioning

confidence: 99%

A shell environment for developing connectionist decision support systems*

et al. 1994

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“…NEULONET (reported in [3] [2]), contains a set of input nodes and output nodes. Every node can take one of the three order pair activation values, (1,O) for true, (0,l) for false, and (0,O) for don't know.…”

Section: Introductionmentioning

confidence: 99%

A connectionist shell for developing expert decision support systems

Quah

Tan²,

Teh³

Proceedings of 1993 IEEE Conference on Tools With Al (TAI-93)

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This paper presents the architecture of a hybrid neural network expert system shell. The system, structured around the concept of "network element", is aimed at preserving semantic structure of the expert system rules whilst incorporating learning capability of neural networks into the inferencing mechanism. Using this architecture, every rule of the knowledge base is represented by a one or two-layer neural network element. These network elements are dynamically linked up to form the rule-tree during inferencing process. The system is also able to adjust its inference strategy according to digererat users and situations. A n editor as also provided to enable easy maintenance of the neural network rule elements. The shell is housed in ( I user friendly rule-based alike interface. Two applications that are built upon the abovementioned shell are discussed, they demonstrated strengths of the network element architecture over conventional rule-based system.

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