Journal of Intelligent & Fuzzy Systems - Volume 6, issue 2

Authors: Wang, Wei | Wang, Zhenyuan | Klir, George J.

Article Type: Research Article

Abstract: A synthetic evaluation of a given object in terms of multiple factors that contribute to some feature of the object (quality, performance, etc.) may be regarded as a system with multiple inputs and one output. Traditionally, the output is expressed as the weighted average of the inputs. Unfortunately, this method is severely limited as it cannot capture any inherent relation among the factors involved. This limitation can be overcome by using the Choquet integral or the fuzzy integral with respect to a fuzzy measure that captures the relation among the factors. The crux of this method is to determine the …right fuzzy measure. In this paper, we describe an efficient genetic algorithm for constructing a suitable fuzzy measure from relevant input–output data. This algorithm has a broad applicability in various problem areas, such as decision making, cluster analysis, pattern recognition, image and speech processing, and expert systems. Show more

Citation: Journal of Intelligent and Fuzzy Systems, vol. 6, no. 2, pp. 171-183, 1998

Authors: Ahn, Sang Chul | Kim, Yong Ho | Kwon, Wook Hyun

Article Type: Research Article

Abstract: In this paper a fuzzy generalized predictive control (FGPC) for nonlinear plants is proposed. In the proposed method, the receding horizon control is applied to the control part, while fuzzy systems are used for the predictor part. It is suggested that the fuzzy predictor is time-varying affine with respect to input variables for easy computation of control inputs. Since the receding horizon control can be obtained only with a predictor instead of a plant model, the fuzzy predictor is obtained directly from input–output data without identifying a plant model. A modified parameter estimation algorithm is used for identifying the fuzzy …predictor. The control inputs of the FGPC are computed by minimizing a receding horizon cost function with predicted plant outputs. The proposed controller has a similar architecture to the generalized predictive control (GPC) except for the predictor synthesis method, and thus could possess inherent good properties of the GPC. It is shown by computer simulation that the performance of the FGPC is satisfactory. Show more

Citation: Journal of Intelligent and Fuzzy Systems, vol. 6, no. 2, pp. 185-207, 1998

Authors: Senjyu, Tomonobu | Molinas, Marta | Shiroma, Takashi | Uezato, Katsumi

Article Type: Research Article

Abstract: This article presents an alternative application of fuzzy control to determine the control signal of Variable Series Capacitor (VSrC) for improving power system stability. VSrC is one of Flexible A.C. Transmission System (FACTS) equipment and has the ability to control the power flow directly by adjusting the condenser capacity. The adjustment of the condenser capacity is made according to the control signal UVSrC which is obtained using the fuzzy reasoning. The effectiveness of the proposed control scheme is demonstrated by computer simulations of the dynamic responses of multi-machine power systems.

Citation: Journal of Intelligent and Fuzzy Systems, vol. 6, no. 2, pp. 209-221, 1998

Authors: Kleyle, Robert M. | de Korvin, Andre

Article Type: Research Article

Abstract: In this paper a methodology for constructing one-step and limiting transition probabilities for fuzzy Markov chains is proposed. This method involves employing Dempster–Shafer type mass functions to construct transition probabilities for set-valued Markov chains in which the sets are subsets of the original state space. The relationship between the composition of mass functions via the Dempster–Shafer rule of combination and set-valued Markov chains is utilized to obtain these transition probabilities. Limiting transition probabilities make use of a limit theorem for the infinite composition of homogeneous mass functions. These set-valued transition probabilities are then converted into transition probabilities on the original …state space. Since more than one sequence of mass functions can be under consideration with varying degrees of certitude, the resulting transition probabilities are typically fuzzy sets. Show more

Citation: Journal of Intelligent and Fuzzy Systems, vol. 6, no. 2, pp. 223-235, 1998

Authors: de Korvin, A. | McKeegan, C. | Kleyle, R.

Article Type: Research Article

Abstract: In this paper we model uncertainty using the so-called rough set approach in which upper and lower approximations of a set of objects are based on equivalence classes determined by attribute values. However, due to imprecision in the information, both the attributes and the resulting decisions are modeled as fuzzy sets. Furthermore, the membership of these fuzzy sets is also fuzzy, creating fuzzy sets of type II. From information of this type, we construct inference rules of unequal strength. The strength of any rule is determined by both its degree of truth and its degree of belief, each of which …are obtained from the fuzzy memberships. Show more

Citation: Journal of Intelligent and Fuzzy Systems, vol. 6, no. 2, pp. 237-244, 1998

Authors: Reza, Ali M. | Doroodchi, Mahmood

Article Type: Research Article

Abstract: Segmentation of step-like signals arises in several application areas; e.g., well-log signal segmentation, ionic-channel signal detection and image segmentation. The objective in processing these signals is to optimally segment the measured signal based on optimization of a criterion function. Recently a sequentially optimum segmentation algorithm is proposed by Moghaddamjoo [13, 14] which performs a reasonable segmentation with affordable computation. This approach, at intermediate and low signal-to-noise ratios (SNRs) has large variances in its estimates. In this work, we apply fuzzy clustering approaches to improve the performance of the aforementioned algorithm at any SNR. In our first approach, we associate …a degree of uncertainty to each of the intermediate decisions made by assigning fuzzy membership functions to samples of the signal. These uncertainties, i.e., membership functions, are updated whenever a new decision is made. At the end, a defuzzification approach is used to crystallize the final segmentation of the signal. In the second approach, the fuzzy algorithm starts when the sequentially optimum approach completes its segmentation. Based on this initial segmentation, the fuzzy algorithm defines a set of membership values for each sample point of the signal. The segmentation is then updated by using these membership values. Based on the new estimates of the parameters of the signal, the memberships are recalculated. This updating is continued iteratively until the parameters of the signal converge. At this time a defuzzification algorithm finalizes the segmentation results. Improvement of the results, due to these modifications is significant, especially at low SNRs. Show more

Citation: Journal of Intelligent and Fuzzy Systems, vol. 6, no. 2, pp. 245-258, 1998

Authors: Vachtsevanos, G.J. | Kim, S.S. | Echauz, J.R. | Ramani, V.K.

Article Type: Research Article

Abstract: This paper describes and compares several nonlinear decision-making systems, including multilayer perceptrons, wavelet neural networks, polynomial neural networks, and fuzzy decision models. The applicability of these systems is illustrated through the problem of check authorization from incomplete data. A benchmark is established in terms of classical linear discriminant analysis and Bayes quadratic classification, in order to assess the need for the neuro-fuzzy strategies. An overall improvement of around 10 percentage points in classification accuracy on an independent test set is demonstrated for each of the neuro-fuzzy models over conventional statistical techniques. In addition to classification accuracy, five performance measures are …reported: accuracy in dollar terms, robustness, parametric efficiency, training computational expense, and classification balance. Even though each system performs differently on these measures, any neuro-fuzzy model is recommended over traditional techniques in problems such as check authorization, where the improvement in reliability warrants the added cost of implementation. Show more

Citation: Journal of Intelligent and Fuzzy Systems, vol. 6, no. 2, pp. 259-278, 1998