The presented control scheme utilizes Adaptive Neuro Fuzzy Inference System (ANFIS) controller to track rotational speed of a reference engine and disturbance rejection during engine idling. To evaluate the performance of the controller a model of the system is developed and simulation results are presented. It is shown that the ANFIS controller is suitable for control systems with large time d...

Swelling in compacted soils may lead to some damages structures and buildings. For the sake of reducing such damages, soil swelling should be determined, so as make exhibit adequate resistance against a phenomenon. most cases, fully non-linear relations have been observed between parameters contributing soil. As such, determined via either experimentations or prediction models. However, being e...

Data mining is the process of extracting hidden patterns from huge data. Among the various clustering algorithms, k-means is the one of most widely used clustering technique in data mining. The performance of k-means clustering depends on the initial clusters and might converge to local optimum. K-means does not guarantee the unique clustering because it generates different results with randoml...

This paper presents a regularized fuzzy c-means clustering method for brain tissue segmentation from magnetic resonance images. A regularizer of the total variation type is explored and a method to estimate the regularization parameter is proposed. 2007 Elsevier B.V. All rights reserved.

MOTIVATION Clustering analysis of data from DNA microarray hybridization studies is essential for identifying biologically relevant groups of genes. Partitional clustering methods such as K-means or self-organizing maps assign each gene to a single cluster. However, these methods do not provide information about the influence of a given gene for the overall shape of clusters. Here we apply a fu...

This paper presents a new algorithm for building an adaptive neuro-fuzzy inference system (ANFIS) from a training data set called B-ANFIS. In order to increase accuracy of the model, the following issues are executed. Firstly, a data merging rule is proposed to build and perform a data-clustering strategy. Subsequently, a combination of clustering processes in the input data space and in the jo...

The kernelized fuzzy c-means algorithm uses kernel methods to improve the clustering performance of the well known fuzzy c-means algorithm by mapping a given dataset into a higher dimensional space non-linearly. Thus, the newly obtained dataset is more likely to be linearly seprable. However, to further improve the clustering performance, an optimization method is required to overcome the drawb...

We introduce a new clustering method for DNA microarray data that is based on space filling curves and wavelet denoising. The proposed method is much faster than the established fuzzy c-means clustering because clustering occurs in one dimension and it clusters cells that contain data, instead of data themselves. Moreover, preliminary evaluation results on data sets from Small Round Blue-Cell t...