A fuzzy rule base is proposed to navigate multi-agents from initial positions to target positions in unknown environments. The proposed fuzzy rule base determines the highest priority of nine possible heading directions. The fuzzy rule base has been developed employing genetic algorithms as an approach to dynamic path planning of autonomous multi-agents in unknown environments. Paths which sati...

Two conflicting goals are often involved in the design of fuzzy rule-based systems: Accuracy maximization and interpretability maximization. A number of approaches have been proposed for finding a fuzzy rule-based system with a good accuracy-interpretability tradeoff. Formulation of the accuracy maximization is usually straightforward in each application area of fuzzy rule-based systems such as...

Fuzzy inference systems provide a simple yet effective solution to complex non-linear problems, which have been applied to numerous real-world applications with great success. However, conventional fuzzy inference systems may suffer from either too sparse, too complex or imbalanced rule bases, given that the data may be unevenly distributed in the problem space regardless of its volume. Fuzzy i...

Text Document are tremendously increasing in the internet, the hierarchical document clustering has proven to be useful in grouping similar document for large applications. Still most documents suffer from problems of high dimensionality, scalability, accuracy and meaningful cluster labels. In this paper an new approach fuzzy frequent itemsets based hierarchical clustering is proposed, in which...

Decision trees are commonly employed as data classifiers in various research fields, but also in real-world application domains. In the fuzzy neural framework, decision trees can offer valuable assistance in determining a proper initial system structure, which means not only feature selection, but also rule extraction and organization. This paper proposes a synergistic model that combines the a...

In this paper an accurate real-time sequence-based system for representation, recognition and analysis of lowintensity facial expressions and FAUs is presented. The feature extraction is done using facial feature point tracking and biased discriminant analysis as an efficient dimension reduction method. A novel classification scheme based on neuro-fuzyy modeling of the FAU intensity is presente...

This paper describes a framework that utilizes an adaptive network based fuzzy inference system (ANFIS) to perform user constrained pattern recognition on time series data. Using a customizable fuzzy logic grammar, the architecture allows an analyst to capture domain expertise in a context relevant manner. Fuzzy logic rules constructed by the analyst are used to perform feature extraction and i...

A model for grey-tone image enhancement using the concept of fuzzy sets is suggested. It involves primary enhancement, smoothing, and then final enhancement. The algorithm for both the primary and final enhancements includes the extraction of fuzzy properties corresponding to pixels and then successive applications of the fuzzy operator "contrast intensifier" on the property plane. The three di...

In the field of control systems it is common to use techniques based on model adaptation to carry out control for plants for which mathematical analysis may be intricate. Increasing interest in biologically inspired learning algorithms for control techniques such as Artificial Neural Networks and Fuzzy Systems is in progress. In this line, this paper gives a perspective on the quality of result...

A new intelligent nonlinear control for power system stabilizers that improves the transient stability is proposed. To guarantee high performance with low complexity cost, new concepts on the passivity design under unknown disturbance inputs, as well as on the adaptive fuzzy logic rule extraction are introduced. This permits the most possible simple design implementation of an adaptive-fuzzy lo...