A. H. Hadjahmadi, M. M. Homayounpour, S. M. Ahadi,
Volume 8, Issue 2 (6-2012)
Abstract
Nowadays, the Fuzzy C-Means method has become one of the most popular
clustering methods based on minimization of a criterion function. However, the
performance of this clustering algorithm may be significantly degraded in the presence of
noise. This paper presents a robust clustering algorithm called Bilateral Weighted Fuzzy CMeans
(BWFCM). We used a new objective function that uses some kinds of weights for
reducing the effect of noises in clustering. Experimental results using, two artificial
datasets, five real datasets, viz., Iris, Cancer, Wine, Glass and a speech corpus used in a
GMM-based speaker identification task show that compared to three well-known clustering
algorithms, namely, the Fuzzy Possibilistic C-Means, Credibilistic Fuzzy C-Means and
Density Weighted Fuzzy C-Means, our approach is less sensitive to outliers and noises and
has an acceptable computational complexity.
H. Kiani Rad, Z. Moravej,
Volume 15, Issue 3 (9-2019)
Abstract
In this paper, a new method is conducted for incorporating the forecasted load uncertainty into the Substation Expansion Planning (SEP) problem. This method is based on the fuzzy clustering, where the location and value of each forecasted load center is modeled by employing the probability density function according to the percentage of uncertainty. After discretization of these functions, the location and value of each of the new load centers are determined based on the presented fuzzy clustering based algorithm. A Genetic Algorithm (GA) is used to solve the presented optimization problem in which the allocations and capacities of new substations as well as the expansion requirements for the existing ones are determined. With the innovative presented method, the impact of uncertainty of the power and location of the predicted loads on the results of SEP is measured, and finally, it is possible to make a proper decision for the SEP. The significant features of this method can be outlined as its applicability to large-scale networks, robustness to load changes, the comprehensiveness and also, the simplicity of applying this method to various problems. The effectiveness of proposed method is demonstrated by application on a real sub-transmission system.
