In this paper fuzzy rule inconsistency resolution and fuzzy rule insertion methods are proposed for fuzzy neural networks. Necessity support and possibility support (referred to as support pair) are applied to detect and remove inconsistencies. In addition to the support pair, the concept of initial learning point is used to handle rule insertion. We demonstrate the use of the proposed methods ...

This paper proposes a complexity management methodology for fuzzy systems with feedback rule bases. The methodology is based on formal methods for presentation, manipulation and transformation of fuzzy rule bases. First, Boolean matrices are used for formal presentation of rule bases. Then, binary merging operations are used for formal manipulation of rule bases. Finally, repetitive merging ope...

HRI (Human–Robot Interaction) is often frequent and intense in assistive service environment and it is known that realizing humanfriendly interaction is a very difficult task because of human presence as a subsystem of the interaction process. After briefly discussing typical HRI models and characteristics of human, we point out that learning aspect would play an important role for designing th...

This paper presents an evolutionary Multiobjective learning model achieving positive synergy between the Inference System and the Rule Base in order to obtain simpler and still accurate linguistic fuzzy models by learning fuzzy inference operators and applying rule selection. The Fuzzy Rule Based Systems obtained in this way, have a better trade-off between interpretability and accuracy in ling...

We study the problem of reconstructing binary images from four projections data in a fuzzy environment. Given the uncertainly projections,w e want to find a binary image that respects as best as possible these projections. We provide an iterative algorithm based on fuzzy integer programming and linear membership functions.

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...

This work presents a method based on an adaptive neuro-fuzzy inference system (ANFIS) for modeling protein secondary structure prediction which aims at acquiring the unknown structure information of target protein directly from its sequence data which is available. The number of input variables and inference rules are commonly too large, sometimes even huge, to make the model building feasible....

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...

In this paper, we develop a curved search algorithm which uses second-order information, for the learning algorithm for a supervised neural network. With the objective of reducing the training time, we introduce a fuzzy controller for adjusting the first and second-order approximation parameters in the iterative method to further reduce the training time and to avoid the spikes in the learning ...