http://ieeexplore.ieee.org TOC Alert for Publication# 91 2010 March 04 18 1 C1 C1 108 18 1 C2 C2 41 18 1 1 13 1192 r rules, r species (swarms) are formed in the upper level, where one species optimizes a single fuzzy rule. Initially, there are no species in HCMSPSO. An online cluster-based algorithm is proposed to generate new species (fuzzy rules) automatically. In the lower layer, subspecies within the same species are formed adaptively in each iteration during the particle update. Several simulations are conducted to verify HCMSPSO performance. Comparisons with other neural learning, genetic, and PSO algorithms demonstrate the superiority of HCMSPSO performance.]]> 18 1 14 26 552 18 1 27 39 803 18 1 40 56 610 w _j ¿ w _k for j < k. We consider a number of different classes of OWA norms. It is shown that the functional generation of the weights of an OWA norm requires the weight generating function have a non-positive second derivative. We discuss the use of the generalized OWA operator to provide norms. Finally we describe the use of OWA operators to induce similarity measures.]]> 18 1 57 66 346 k-means partition is derived by using a noise-rejection mechanism based on the noise-clustering approach. The responsibility weight of each sample for the k-means process is estimated by considering the noise degree of the sample, and cluster indicators are calculated in a fuzzy principal-component-analysis (PCA) guided manner, where fuzzy PCA-guided robust k-means is performed by considering responsibility weights of samples. Then, the proposed method achieves cluster-core estimation in a deterministic way. The validity of the derived cluster cores is visually assessed through distance-sensitive ordering, which considers responsibility weights of samples. Numerical experiments demonstrate that the proposed method is useful for capturing cluster cores by rejecting noise samples, and we can easily assess cluster validity by using cluster-crossing curves.]]> 18 1 67 79 547 18 1 80 93 1178 The power-average (PA) operator and the power-ordered-weighted-average (POWA) operator are the two nonlinear weighted-average aggregation tools whose weighting vectors depend on the input arguments. In this paper, we develop a power-geometric (PG) operator and its weighted form, which are on the basis of the PA operator and the geometric mean, and develop a power-ordered-geometric (POG) operator and a power-ordered-weighted-geometric (POWG) operator, which are on the basis of the POWA operator and the geometric mean, and study some of their properties. We also discuss the relationship between the PA and PG operators and the relationship between the POWA and POWG operators. Then, we extend the PG and POWG operators to uncertain environments, i.e., develop an uncertain PG (UPG) operator and its weighted form, and an uncertain power-ordered-weighted-geometric (UPOWG) operator to aggregate the input arguments taking the form of interval of numerical values. Furthermore, we utilize the weighted PG and POWG operators, respectively, to develop an approach to group decision making based on multiplicative preference relations and utilize the weighted UPG and UPOWG operators, respectively, to develop an approach to group decision making based on uncertain multiplicative preference relations. Finally, we apply both the developed approaches to broadband Internet-service selection. ]]> 18 1 94 105 332 18 1 106 113 291 Cognitive maps (CMs), fuzzy cognitive maps (FCMs), and dynamical cognitive networks (DCNs) are related tools for modeling the cognition of human beings and facilitating machine inferences accordingly. FCMs extend CMs, and DCNs extend FCMs. Domain experts often face the challenge that CMs/FCMs are not sufficiently capable in many applications and that DCNs are too complex. This paper presents a simplified DCN (sDCN) that extends the modeling capability of FCM/CM, yet maintains simplicity. Additionally, this paper proves that there exists a theoretical equivalence among models in the cognitive map family of CMs, FCMs, and sDCNs. It shows that every sDCN can be represented by an FCM or a CM, and vice versa; similarly, every FCM can be represented by a CM, and vice versa. The result shows that CMs, FCMs, and sDCNs are a family of cognitive models that differs from many extended models. This paper also provides a constructive approach to transforming one cognitive map model into other cognitive map models in the family. Therefore, domain experts are able to model applications with more descriptive sDCNs and leave theoretical analysis to the simpler CM forms. The existence of theoretical transformation links among the models provides strong support for their theoretical analysis and flexibility in their applications. ]]> 18 1 114 124 971 This paper presents the stability analysis of fuzzy-model-based (FMB) control systems. Staircase membership functions are introduced to facilitate the stability analysis. Through the staircase membership functions approximating those of the fuzzy model and fuzzy controller, the information of the membership functions can be brought into the stability analysis. Based on the Lyapunov-stability theory, stability conditions in terms of linear-matrix inequalities (LMIs) are derived in a simple and easy-to-understand manner to guarantee the system stability. The proposed stability-analysis approach offers a nice property that includes the membership functions of both fuzzy model and fuzzy controller in the LMI-based stability conditions for a dedicated FMB control system. Furthermore, the proposed stability-analysis approach can be applied to the FMB control systems of which the membership functions of both fuzzy model and fuzzy controller are not necessarily the same. Greater design flexibility is allowed to choose the membership functions during the design of fuzzy controllers. By employing membership functions with simple structure, it is possible to lower the structural complexity and the implementation cost. Simulation examples are given to illustrate the merits of the proposed approach. ]]> 18 1 125 137 474 This paper proposes the design of fuzzy-rule-based systems using continuous ant-colony optimization (RCACO). RCACO determines the number of fuzzy rules and optimizes all the free parameters in each fuzzy rule. It uses an online-rule-generation method to determine the number of rules and identify suitable initial parameters for the rules and then optimizes all the free parameters using continuous ant-colony optimization (ACO). In contrast to traditional ACO, which optimizes in the discrete domain, the RCACO optimizes parameters in the continuous domain and can achieve greater learning accuracy. In RCACO, the path of an ant is regarded as a combination of antecedent and consequent parameters from all the rules. A new path-selection method based on pheromone levels is proposed for initial-solution construction. The solution is modified by sampling from a Gaussian probability-density function and is then refined using the group best solution. Simulations on fuzzy control of three nonlinear plants are conducted to verify RCACO performance. Comparisons with other swarm intelligence and genetic algorithms demonstrate the advantages of RCACO. ]]> 18 1 138 149 1596 Robust adaptive-fuzzy-tracking control of a class of uncertain multi-input/multi-output nonlinear systems with coupled interconnections is considered in this paper. Takagi–Sugeno (T–S) fuzzy systems are used to approximate the unknown system functions. A novel adaptive-control scheme is developed on the basis of the so-called “dynamic-surface control” and “minimal-learning parameters” techniques. The proposed scheme has following two key features. First, the number of parameters updated online for each subsystem is reduced to one, and both problems of “curse of dimension” for high-dimensional systems and “explosion of complexity” inherent in the conventional backstepping methods are circumvented. Second, the potential controller-singularity problem in some of the existing adaptive-control schemes with feedback-linearization techniques is overcome. It is shown via Lyapunov theory that all the signals in the closed-loop system are semiglobally uniformly ultimately bounded. Finally, simulation results via two examples are presented to demonstrate the effectiveness and advantages of the proposed scheme. ]]> 18 1 150 160 447 In this paper, a multiobjective genetic fuzzy system (GFS) to learn the granularities of fuzzy partitions, tuning the membership functions (MFs), and learning the fuzzy rules is presented. It uses dynamic constraints, which enable three-parameter MF tuning to improve the accuracy while guaranteeing the transparency of fuzzy partitions. The fuzzy models (FMs) are initialized by a method that combines the benefits of Wang–Mendel (WM) and decision-tree algorithms. Thus, the initial FMs have less rules, rule conditions, and input variables than if WM initialization were to be used. Moreover, the fuzzy partitions of initial FMs are always transparent. Our approach is tested against recent multiobjective and monoobjective GFSs on six benchmark problems. It is concluded that the accuracy and interpretability of our FMs are always comparable or better than those in the comparative studies. Furthermore, on some benchmark problems, our approach clearly outperforms some comparative approaches. Suitability of our approach for higher dimensional problems is shown by studying three benchmark problems that have up to 21 input variables. ]]> 18 1 161 177 2201 The Choquet and the Sugeno integral provide a useful tool in many problems in engineering and social choice where the aggregation of data is required. However, their applicability is restricted because of the special operations used in the construction of these integrals. Therefore, we provide a concept of integrals generalizing both the Choquet and the Sugeno case. For functions with values in the nonnegative real numbers, universal integrals are introduced and investigated, which can be defined on arbitrary measurable spaces and for arbitrary monotone measures. For a fixed pseudomultiplication on the nonnegative real numbers, the smallest and the greatest universal integrals are given. Finally, another construction method for obtaining universal integrals is introduced, and the restriction to the unit interval, i.e., to fuzzy integrals, is considered. ]]> 18 1 178 187 422 18 1 188 194 443 18 1 194 201 336 This paper is concerned with $H_infty$-design for a class of networked control systems (NCSs) with multiple state-delays via the Takagi–Sugeno (T–S) fuzzy model. The transfer delays and packet loss that are induced by the limited bandwidth of communication networks are considered. The focus of this paper is on the analysis and design of a full-order $H_infty$ filter, such that the filtering-error dynamics are stochastically stable, and a prescribed $H_infty$ attenuation level is guaranteed. Sufficient conditions are established for the existence of the desired filter in terms of linear-matrix inequalities (LMIs). An example is given to illustrate the effectiveness and applicability of the proposed design method. ]]> 18 1 201 208 359 This study suggests the use of fuzzy-filtering algorithms to deal with the uncertainties associated to the analysis of physiological signals. The signal characteristics, for a given situation or physiological state, vary for an individual over time and also vary among the individuals with the same state. These random variations are due to the several factors related to the physiological behavior of individuals, which cannot be taken into account in the interpretation of signal characteristics. Our approach is to reduce the effect of random variations on the analysis of signal characteristics via filtering out randomness or uncertainty from the signal using a nonlinear fuzzy filter. A fuzzy-filtering algorithm, which is based on a modification of filtering algorithm of Kumar et al. [M. Kumar, N. Stoll, and R. Stoll, IEEE Trans. Fuzzy Syst., vol. 17, no. 1, pp. 150–166, Feb. 2009], is proposed for an improved performance. The method is illustrated by studying the effect of head-up tilting on the heart-rate signal of 40 healthy subjects. ]]> 18 1 208 216 1798 18 1 217 226 5229 This comment tries to describe a theoretical mistake made in the aforementioned paper [G. Feng, IEEE Trans. Fuzzy Syst., vol. 11, no. 5, pp. 605–612, Oct. 2003] to formulate the inverse of matrices used to construct the piecewise-quadratic Lyapunov functions. Derivation of these inverse matrices is the most critical step toward transforming the design constraints into linear matrix inequalities (LMIs). Therefore, the erroneous formulation essentially affects the validity of the approach and final results. Unfortunately, it seems that there is no simple correction for this problem. However, some close alternative approaches are suggested by the same authors in their other works. ]]> 18 1 227 228 145 IEEE Trans. Fuzzy Syst., vol. 17, no. 5, pp. 1189-1207, Oct. 2009), and because one of the errors relates the term ¿ ¿-plane¿ to the term ¿z -slice,¿ it also connects these two terms more correctly.]]> 18 1 229 230 167 18 1 231 232 1065 18 1 C3 C3 37 18 1 C4 C4 29