"This paper presents an approach to integrate multiple fuzzy
knowledge bases for increasing the accuracy and decreasing the
complexity of the integrated knowledge base. The proposed
approach consists of two phases: PSO -based fuzzy knowledge
encoding, and PSO-based fuzzy knowledge fusion. In the
encoding phase, the fuzzy rule sets and fuzzy sets with its
corresponding membership functions are encoded as a string and
are put together in the initial knowledge population. In the fusion
phase, the particle swarm algorithm is used to explore the fuzzy
rule sets, fuzzy sets and membership functions to its optimal or
the approximately optimal extent. Two application domains,
including diagnosis on student’s program learning style and
situational learning services composition, were used to
demonstrate the performance of the proposed knowledge
integration approach. Experiment results revealed that our
approach will effectively increase the accuracy and decrease the
complexity of integrated knowledge base. The results of this
study could extend the effectiveness of knowledge inference and
decision making."
