Simple Fuzzy Systems Research Articles

A Multitasking Genetic Algorithm for Mamdani Fuzzy System with Fully Overlapping Triangle Membership Functions

Evolutionary multitasking is an emerging subject in the field of evolutionary computation. By adopting methods to effectively discover and implicitly transfer useful genetic materials from one task to another, it can process multiple optimization tasks simultaneously using one evolutionary calculation. Inspired by the idea of evolutionary multitasking, it can be also used in optimization problems of fuzzy systems (FSs). By exchanging optimization experience and knowledge between different FSs, it is expected to enhance the speed and efficiency of FS optimization and be applied to FS optimization tasks with higher requirement for running time and accuracy of results. Moreover, using the experience and knowledge of simple FSs optimization tasks to facilitate the optimization of complex FSs, it can resolve high time consuming and high cost that triggered by large, complex FSs optimization problems and improve the feasibility of its application in large complex fuzzy control optimization problems. Different from the general multi-task learning, the multi-task learning of FS optimization has its own features. Consequently, based on the thought of evolutionary multitasking and the traits of multi-task learning of FS optimization, a general framework of multitasking genetic fuzzy system (MTGFS) is proposed to effectively solve the multi-task optimization problems of fuzzy systems. A multitasking evolutionary optimization algorithm for Mamdani fuzzy systems with fully overlapping triangle membership functions (FOTMF-M-MTGFS) is also designed and implemented. Comparative studies with genetic fuzzy system (GFS), a single-task optimization algorithm of FSs, indicate that the evolution speed and result of the MTGFS are superior than GFS on average.

A Dempster Shafer-based fuzzy multisensor fusion system using airborne lidar and hyperspectral imagery

ABSTRACTNowadays, accurate spectral reflectance information is provided by hyperspectral (HS) data while light detection and ranging (lidar) data provides precise information about the height and geometrical properties of the surfaces. In the most research papers, data fusion of disparate sensors significantly improves object classification performance compared to that of just an individual sensor. Previous researches on fusion of these two sensors had problems such as crisp classifiers or simple fuzzy decision-making systems. This article tries to overcome these weaknesses by accurate support vector machine (SVM) and Fuzzy SVM as classifiers in crisp and fuzzy decision fusion system and fusion of two sensors by two different methods based on precise theories of Bayesian and Shafer. Also, the proposed method tries to compare the results of fusion of both data using decision fusion system with stacked features strategy. This study focuses on HS and lidar fusion through three main phases. The first phase is based on the using of Noise Weighted Harsanyi-Farrand-Chang method and principal component analysis to overcome the high dimensionality problem of HS data. The second phase is based on the feature extraction and selection strategy on lidar data. Finally, fuzzy SVM and Dempster Shafer methods are applied as fuzzy classification and fuzzy decision fusion strategies on the feature spaces. A co-registered HS and lidar data set from Houston of U.S.A. by 15 classes was available to examine the effectiveness of the proposed method. The results of this study highlight that the combination of HS and lidar data enable reliable mapping of land cover.

ON GENERALIZED FUZZY MULTISETS AND THEIR USE IN COMPUTATION

An orthogonal approach to the fuzzication of both multisets and hybrid sets is presented. In particular, we introduce L-multi-fuzzy and L-fuzzy hybrid sets, which are general enough and in spirit with the basic concepts of fuzzy set theory. In addition, we study the properties of these structures. Also, the usefulness of these structures is examined in the framework of me- chanical multiset processing. More specically, we introduce a variant of fuzzy P systems and, since simple fuzzy membrane systems have been introduced elsewhere, we simply extend previously stated results and ideas.

Hierarchical genetic fuzzy systems

Abstract This paper introduces a hierarchical evolutionary approach to optimize the parameters of Takagi–Sugeno (TS) fuzzy systems. The approach includes a least-squares method to determine the parameters of nonlinear consequents. A pruning procedure is developed to avoid redundancy in each rule consequent and to achieve proper representation flexibility. The performance of the hierarchical evolutionary approach is evaluated using function approximation and classification problems. They demonstrate that the evolutionary algorithm, working together with optimization and pruning procedures, provides structurally simple fuzzy systems whose performance seems to be better than the ones produced by alternative approaches.

Local and global bifurcations in simple Takagi-Sugeno fuzzy systems

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, degenerate) are detected and summarized in the corresponding bifurcation diagrams. It is claimed,that this kind of careful analysis could help to cope with some criticism raised regarding the blind use of fuzzy systems.

Application of the self-organising fuzzy control to an electrohydraulic servocylinder drive with a vertical load

The self-organising fuzzy controller can maintain its control effect to a certain extent, because it has the capability of online learning and modifying its control rules in response to the change of the working environment. In this paper, the self-organising fuzzy control approach is applied to an electrohydraulic position/speed servocylinder drive with a vertical load. The performance of the system controlled by the self-organizing fuzzy controller is compared with those of the conventional P, PI and simple fuzzy control systems. The experimental results show that the system with the self-organising fuzzy controller exhibits better performances than those for the systems with P, PI and simple fuzzy controllers whether under normal operating conditions or in face of plane parameter variations.