Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Samuel Y. E. Lim

dblp:79/2848 · DBLP profile ↗
← Back
1ranked-venue papers
0as first author
0since 2021 · last 1998
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1Systems, architecture and hardware · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Knowledge representation and reasoning · 100%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Knowledge, reasoning and agents › Knowledge representation and reasoning › uncertainty reasoning
fuzzy reasoning
0.011998
Integration of Knowledge-Based Systems and Neural Networks: Neuro-Expert Petri Net Models and Applications · ICRA 1998
Knowledge, reasoning and agents › Knowledge representation and reasoning
knowledge-based systems
0.011998
Integration of Knowledge-Based Systems and Neural Networks: Neuro-Expert Petri Net Models and Applications · ICRA 1998

Methods — techniques the papers use, named apart from their topics

fuzzy logic · 0.0expert petri net · 0.0artificial neural network · 0.0
YearPublicationVenuePosition
1998 Integration of Knowledge-Based Systems and Neural Networks: Neuro-Expert Petri Net Models and Applications
abstract
This paper identifies and describes how knowledge based systems (KBSs), fuzzy logic and artificial neural networks (ANNs) can be integrated, and provides a novel (fuzzy) expert Petri net (EPN (FEPN)) based approach for the integration of KBSs and ANNs. A generic expert Petri net model of single neuron is presented, and a two-layer generic Petri net model for (fuzzy) neural networks and neural (fuzzy) expert Petri nets (NEPN, NFEPN)) that use this neuron model as a building block is described. The NEPN and NFEPN models can be used for representing a (fuzzy) knowledge base and (fuzzy) reasoning. Also, they can be utilized to develop ANN-like multilayered Petri net architectures of distributed hybrid intelligence having learning ability. Some application examples are illustrated for validation of the proposed models.
Xuan F. Zha, Samuel Y. E. Lim, Sai Cheong Fok
ICRA2