The relevance of bifurcation analysis in Takagi–Sugeno (T–S) fuzzy systems is emphasized mainly through examples. It is demonstrated that even the most simple cases can show a great variety of behaviors. To understand the richness of asymptotic dynamics, one can find in T–S systems, a methodology is proposed by invoking bifurcation theory. Several local and global bifurcations (some of them, de...

—— This paper describes the preliminary research and implementation of an experimental test bench set up for an electric vehicle Antilock Braking System (ABS)/Traction Control System (TCS) representing the dry, wet and icy road surfaces. A Fuzzy logic based controller to control the wheel slip for electric vehicle antilock braking system is presented. The test facility comprised of an induction...

nThis paper transmits a FORTRAN-IV coding of the fuzzy c-means (FCM) clustering program. The FCM program is applicable to a wide variety of geostatistical data analysis problems. This program generates fuzzy partitions and prototypes for any set of numerical data. These partitions are useful for corroborating known substructures or suggesting substructure in unexplored data. The clustering crit...

The modelling of real-world complex systems is an area of ongoing interest for the research community. Real-world systems present a variety of challenges not least of which is the problem of uncertainty inherent in their operation. In this research the problem of inventory management was chosen. The goal was to discover a suitable configuration for a Simulated Annealing search with a fuzzy inve...

Clustering is a task of assigning a set of objects into groups called clusters. In general the clustering algorithms can be classified into two categories. One is hard clustering; another one is soft (fuzzy) clustering. Hard clustering, the data’s are divided into distinct clusters, where each data element belongs to exactly one cluster. In soft clustering, data elements belong to more than one...

A measure of s k c e s s for any learning algorithm is how u s e ful it is in a variety of learning situations. Those learning algorithms that support universal function approximation can theoretically h e applied to a very large and interesting class of learning problems. Many kinds of neural network architectures have already been shown to support universal approximation. In this paper, we wi...

This paper describes how cognitive methods, such as Fuzzy Controllers, Arti cial Neural Networks, and Genetic Algorithms can be integrated together to produce a family of Hybrid Intelligent Controllers, which provide good performance in a variety of complex, real-time and non-linear problems. Two new paradigms are introduced, namely Weighted Radial Basis Functions and Hierarchical Hybrid Fuzzy ...

Large data sets are divided into clusters of smaller the same using a technique called clustering. One artificial intelligence decision-making techniques with broad variety uses is fuzzy logic. It has been established that logic works well in practically all systematic disciplines. In realm medical imaging, C-means clustering algorithm widely used unsupervised method. This research suggests met...

Biometric techniques are gaining importance for personal authentication and identification as compared to the traditional authentication methods. Biometric templates are vulnerable to variety of attacks due to their inherent nature. When a person’s biometric is compromised his identity is lost. In contrast to password, biometric is not revocable. Therefore, providing security to the stored biom...

Data mining technology has emerged as a means for identifying patterns and trends from large quantities of data. Clustering is a primary data description method in data mining which group’s most similar data. The data clustering is an important problem in a wide variety of fields. Including data mining, pattern recognition, and bioinformatics. It aims to organize a collection of data items into...