TY - GEN
T1 - Towards Adaptive Interpolative Reasoning
AU - Yang, Longzhi
AU - Shen, Qiang
N1 - L. Yang and Q. Shen. Towards Adaptive Interpolative Reasoning. Proceedings of the 18th International Conference on Fuzzy Systems (FUZZ-IEEE'09), pp. 542-549, 2009.
PY - 2009/8
Y1 - 2009/8
N2 - Fuzzy interpolative reasoning has been extensively
studied due to its ability to enhance the robustness of fuzzy
systems and to reduce system complexity. However, during
the interpolation process, it is possible that multiple object
values for a common variable are inferred which may lead
to inconsistency in interpolated results. Such inconsistencies
may result from defective interpolated rules or incorrect interpolative
transformations. This paper presents a novel approach
for identification and correction of defective rules in transformations,
thereby removing the inconsistencies. In particular,
an assumption-based truth maintenance system (ATMS) is
used to record dependencies between reasoning results and
interpolated rules, while the underlying technique that the
general diagnostic engine (GDE) employs for fault localization
is adapted to isolate possible faulty interpolated rules and
their associated interpolative transformations. From this, an
algorithm is introduced to allow for the modification of the
original linear interpolation to become first-order piecewise
linear. The approach is applied to a carefully chosen practical
problem to illustrate the potential in strengthening the power
of interpolative reasoning.
AB - Fuzzy interpolative reasoning has been extensively
studied due to its ability to enhance the robustness of fuzzy
systems and to reduce system complexity. However, during
the interpolation process, it is possible that multiple object
values for a common variable are inferred which may lead
to inconsistency in interpolated results. Such inconsistencies
may result from defective interpolated rules or incorrect interpolative
transformations. This paper presents a novel approach
for identification and correction of defective rules in transformations,
thereby removing the inconsistencies. In particular,
an assumption-based truth maintenance system (ATMS) is
used to record dependencies between reasoning results and
interpolated rules, while the underlying technique that the
general diagnostic engine (GDE) employs for fault localization
is adapted to isolate possible faulty interpolated rules and
their associated interpolative transformations. From this, an
algorithm is introduced to allow for the modification of the
original linear interpolation to become first-order piecewise
linear. The approach is applied to a carefully chosen practical
problem to illustrate the potential in strengthening the power
of interpolative reasoning.
U2 - 10.1109/FUZZY.2009.5277053
DO - 10.1109/FUZZY.2009.5277053
M3 - Conference Proceeding (Non-Journal item)
SP - 542
EP - 549
BT - Proceedings of the 18th International Conference on Fuzzy Systems (FUZZ-IEEE'09)
ER -