Publications

Control engineers are in need of simpler process and plant models and controller design methods, far removed from the highly sophisticated mathematical models available and their underlying rigorous assumptions. These simpler design methods should provide good performance characteristics, and they should be robust enough with regard to disturbances, parameter uncertainties, and unmodeled structural properties of the process under control. Fuzzy control provides a variety of design methods which can, together with traditional control techniques, cope with modern control problems. This section gives a short overview of the history and motivation of fuzzy control. The design goal of fuzzy control and the problems of stabilization, tracking, and performance are briefly discussed. Starting with a definition of a fuzzy region in state space the most important fuzzy control techniques are introduced.

In this study, a nonlinear blind channel equalization is implemented by using a Modified Fuzzy C-Means (MFCM) algorithm. The proposed MFCM searches the optimal channel output states of a nonlinear channel from the received symbols, based on the Bayesian likelihood fitness function instead of a conventional Euclidean distance measure. In its searching procedure, all of the possible desired channel states are constructed by the combinations of estimated channel output states. The desired state with the maximum Bayesian fitness is selected and placed at the center of a Radial Basis Function (RBF) equalizer to reconstruct transmitted symbols. In the simulations, binary signals are generated at random with Gaussian noise. The performance of the proposed method is compared with that of a hybrid genetic algorithm (GA augment by simulated annealing (SA), GASA). It is shown that a relatively high accuracy and fast search speed has been achieved. Key words: Nonlinear blind channel equalization, Modified Fuzzy C-Means, RBF equalizer, Channel output states 1.

To address the situation where the complete consistency is unnecessary, a stepwise optimization model-based method for testing the acceptably additive consistency (AAC) of hesitant fuzzy preference relations (HFPRs) is introduced. Then, an AAC concept for HFPRs is defined. Meanwhile, incomplete HFPRs (iHFPRs) are discussed and a series of optimization models to acquire complete HFPRs is constructed. If the consistency is unacceptable, an optimization model for revising unacceptably consistent HFPRs under the conditions of the AAC and maximizing the ordinal consistency (OC) is offered. Subsequently, a model for minimizing the number of adjusted variables is presented. Considering the weighting information and the consensus for group decision making (GDM), the weights of fuzzy preference relations (FPRs) obtained from each individual HFPR and the decision makers (DMs) are determined using the distance measure. With regard to the consensus, two models for reaching the consensus requirement and minimizing the amount of revised variables are separately constructed, which are both based on the analysis of maximizing the OC. Furthermore, the thresholds of the additive consistency and the consensus are studied using the Monte Carlo simulation method. A GDM algorithm with HFPRs is offered. Finally, an example and comparison are provided to show the efficiency of the new procedure.

Brace treatment is the most commonly used nonsurgical treatment for adolescents with idiopathic scoliosis. However, brace treatment is not always successful and the factors influencing its success are not completely clear. This makes treatment outcome difficult to predict. A computer model which can accurately predict treatment outcomes could potentially provide valuable treatment recommendations. This paper describes a fuzzy system that includes a prediction model and a decision support engine. The model was constructed using conditional fuzzy c-means clustering to discover patterns in retrospective patient data. The model's ability to predict treatment outcome was compared to the ability of eight Scoliosis experts. The model and experts each predicted treatment outcome retrospectively for 28 braced patients, and these predictions were compared to the actual outcomes. The model outperformed all but one expert individually and performed similarly to the experts as a group. These results suggest that the fuzzy model is capable of providing meaningful treatment recommendations. This study offers the first model for this application whose performance has been shown to be at or above the human expert level.

In this study, we are concerned with a two-objective development of information granules completed on a basis of numeric data. The first goal of this design concerns revealing and representing a structure in a data set. As such, it is oriented toward coping with the underlying relational aspects of the experimental data. The second goal deals with the formation of a mapping between information granules constructed in two spaces (thus, it concentrates on the directional aspect of information granulation). The quality of the mapping is directly affected by the information granules over which it operates, so in essence, we are interested in the granules that not only reflect the data, but also contribute to the performance of such mapping. The optimization of information granules is realized through a collaboration occurring at the level of data and the mapping between the data sets. The operational facet of the problem is cast in the realm of fuzzy clustering. As the standard techniques of fuzzy clustering (including a well-known approach of FCM) are aimed exclusively at the first objective identified above, we augment them in order to accomplish sound mapping properties between the granules. This leads to a generalized version of the FCM (and any other clustering technique for this matter). We propose a generalized version of the objective function that includes an additional collaboration component to make the formed information granules in rapport with the mapping requirements (that comes with a directional component captured by the information granules). The additive form of the objective function with a modifiable component of collaborative activities makes it possible to express a suitable level of collaboration and avoid a phenomenon of potential competition in the case of incompatible structures and the associated mapping. The logic-based type of the mapping (that invokes the use of fuzzy relational equations) comes as a consequence of the logic framework of information granules. A complete optimization method is provided and illustrated with several numeral studies.

In this paper, we develop an advanced architecture and come up with a comprehensive design methodology of genetically optimized hybrid fuzzy neural networks (gHFNNs). The construction of gHFNN exploits fundamental technologies of computational intelligence (CI), namely fuzzy sets, neural networks, and genetic algorithms (GAs). The architecture of the gHFNN results from a highly synergistic usage of the genetic optimization-driven hybrid system being generated by combining fuzzy neural networks (FNNs) with polynomial neural networks (PNN). FNN contributes to the formation of the premise part of the overall network structure of the gHFNN. The consequence part of the gHFNN is designed using PNNs. The optimization of the FNN is realized with the aid of a standard backpropagation learning algorithm and genetic optimization. As the consequence part of the gHFNN, the development of the PNN dwells on two general optimization mechanisms: the structural optimization is realized via GAs whereas in the case of the parametric optimization we proceed with a standard least square method-based learning (optimization). Through the consecutive process of such structural and parametric optimization, an optimized PNN becomes generated in a dynamic fashion. To evaluate the performance of the gHFNNs, we experimented with a number of representative numerical examples. A comparative analysis demonstrates that the proposed gHFNNs are neurofuzzy systems with higher accuracy as well as more superb predictive capability than other models available in the literature