The objective of this work is to describe a numerical technique to identify parameters of a fuzzy model. When this model and the membership functions of the input variables are continuously differentiable, we show that the estimation of parameters can be performed with a two-levels hierarchical algorithm, comprising the estimation of the model parameters and the estimation of the membership fun...

In this study, we propose a space search algorithm (SSA) and then introduce a hybrid optimization of ANFIS-based fuzzy models based on SSA and information granulation (IG). In comparison with conventional evolutionary algorithms (such as PSO), SSA leads not only to better search performance to find global optimization but is also more computationally effective. In the hybrid optimization of ANF...

Fuzzy-logic-based modelling and control is very efficient in dealing with imprecision and nonlinearity [1]. However, the conventional approaches for designing Fuzzy Inference Systems (FISs) are subjective, which require significant human’s efforts. Other than time consuming, the subjective approaches may not be successful if the system is too complex or uncertain. Therefore, many researchers ha...

Fuzzy models describe nonlinear input-output relationships with linguistic fuzzy rules. A hierarchical fuzzy modeling is promising for identification of fuzzy models of target systems that have many input variables. In the identification, (1) determination of a hierarchical structure of submodels, (2) selection of input variables of each submodel, (3) division of input and output space, (4) tun...

In our previous work we showed that genetic algorithms (GAs) provide a powerful tool to increase the accuracy of fuzzy models for both systems modeling and classification. In addition to these results, we explore the GA to find redundancy in the fuzzy model for the purpose of model reduction. An aggregated similarity measure is applied to search for redundancy in the rule base description. As a...

This paper is concerned with both the problems of quantitative and qualitative modeling of complex systems by using fuzzy techniques. A unified approach for the identification and subsequent extraction of linguistic knowledge of systems using fuzzy relational models is addressed. This approach deals with the identification problem by means of optimal numerical solutions based on weighted least ...

The identification of uncertain and nonlinear systems is an important and challenging problem. Fuzzy logic models are often a good choice to describe such systems, however in many cases these become complex soon. Generally, too less effort is put into variable selection and in the creation of suitable local rules. Moreover, in general no model reduction is applied, while this may simplify the m...

It is known that control signals from a fuzzy logic controller are determined by a response behavior of a controlled object rather than its analytical models. That implies that the fuzzy controller could yield a similar control result for a set of plants with a similar dynamic behavior. This idea leads to modeling of a plant with unknown structure by defining several types of dynamic behavior, ...

To reduce the effect of fuzzy vein edges and tips of the infrared finger vein recognition, this paper presents a tri-value template fuzzy matching algorithm, which segments the vein feature image into three areas: subject area, fuzzy area and background area, and computes the average distance of non-background point to non-background area as the dissimilarity score between the two templates. Th...

This work introduces an embedded small indoor helicopter heading controller based on fuzzy logic. The work addresses the problem of system identification when implementing a Takagi-Sugeno-Kang type fuzzy logic controller. Instead of identifying the system formally beforehand, the fuzzy controllers consequent parameters are learned using a Neuro-Fuzzy Inference System with data collected from an...