Volume 6     Volume 5     Volume 4     Volume 3     Volume 2     Volume 1

Issue 4     Issue 3     Issue 2     Issue 1

The transform image codec based on fuzzy control and human visual system
Ann Wen Kuei, Yen Lu Chung, Chang Tsai Ming
vol. 3, no. 3, pp. 253-9, Aug. 1995

A novel processing scheme for gray level image compression based on the human visual system (HVS) and fuzzy control is proposed. The spatial model of the threshold vision that incorporates the masking processes takes account of two major sensitivities of the human visual system, namely background illumination levels and spatial frequency sensitivities. The distortion measures use common sense fuzzy rules for image quality prediction. The human visual models have been successfully applied in image compression. By the addition of the visual model, performance of these systems has a visible improvement of subject quality depending on visual perception. The processed image performs both improved compression ratio as well as improved SNR (signal to noise ratio) in compression to the standardized still image compression technique, i.e., the JPEG.

Selecting fuzzy if-then rules for classification problems using genetic algorithms
H. Ishibuchi, K. Nozaki, N. Yamamoto, H. Tanaka
vol. 3, no. 3, pp. 260-70, Aug. 1995

This paper proposes a genetic-algorithm-based method for selecting a small number of significant fuzzy if-then rules to construct a compact fuzzy classification system with high classification power. The rule selection problem is formulated as a combinatorial optimization problem with two objectives: to maximize the number of correctly classified patterns and to minimize the number of fuzzy if-then rules. Genetic algorithms are applied to this problem. A set of fuzzy if-then rules is coded into a string and treated as an individual in genetic algorithms. The fitness of each individual is specified by the two objectives in the combinatorial optimization problem. The performance of the proposed method for training data and test data is examined by computer simulations on the iris data of Fisher.

Modeling and control of carbon monoxide concentration using a neuro-fuzzy technique
K. Tanaka, M. Sano, H. Watanabe
vol. 3, no. 3, pp. 271-9, Aug. 1995

Modeling and control of carbon monoxide (CO) concentration using a neuro-fuzzy technique are discussed. A self-organizing fuzzy identification algorithm (SOFIA) for identifying complex systems such as CO concentration is proposed. The main purpose of SOFIA is to reduce the computational requirement for identifying a fuzzy model. In particular, the authors simplify a procedure for finding the optimal structure of fuzzy partition. The delta rule, which is a basic learning method in neural networks, is used for parameter identification of a fuzzy model. SOFIA consists of four stages which effectively realize structure identification and parameter identification. The procedure of SOFIA is concretely demonstrated by a simple example which has been used in some modeling exercises. The identification result shows effectiveness of SOFIA. Next, the authors apply SOFIA to a prediction problem for CO concentration in the air at the busiest traffic intersection in a large city of Japan. Prediction results show that the fuzzy model is much better than a linear model. Furthermore, the authors simulate a control system for keeping CO concentration at a constant level by using the identified fuzzy model. A self-learning method for adaptively modifying controller parameters by delta rule is introduced because the dynamics of real CO concentration system changes gradually over a long period of time. Two self-learning controllers are designed in this simulation. One is a self-learning linear PI controller. The other is a self-learning fuzzy PI controller. The authors investigate robustness and adaptability of this control system for disturbance and parameter perturbation of the CO concentration model. Simulation results show that the self-learning fuzzy controller is more robust and adaptive.

An acquisition of operator's rules for collision avoidance using fuzzy neural networks
I. Hiraga, T. Furuhashi, Y. Uchikawa, S. Nakayama
vol. 3, no. 3, pp. 280-7, Aug. 1995

The procedure for acquiring control rules to improve the performance of control systems has received considerable attention previously. This paper deals with a collision avoidance problem in which the controlled object is a ship with inertia which must avoid collision with a moving object. It has proven to be difficult to obtain collision avoidance rules, i.e., steering rules and speed control rules, which coincide with the operator's knowledge. This paper shows that rules of this type can be acquired directly from observational data using fuzzy neural networks (FNNs). This paper also shows that the FNN can obtain portions of the fuzzy rules for the inferences of the static and dynamic degrees of danger and the decision table based on the degrees of danger to avoid the moving obstacle.

An enhanced two-level Boolean synthesis methodology for fuzzy rules minimization
R. Rovatti, R. Guerrieri, G. Baccarani
vol. 3, no. 3, pp. 288-99, Aug. 1995

A new methodology for the minimization of a given set of fuzzy rules is presented. It is based on a novel mapping of fuzzy relations on Boolean functions and exploits existing Boolean synthesis algorithms. In this mapping each fuzzy membership predicate is translated into a Boolean variable and proper constraints on Boolean manipulations are added to guarantee fuzziness translation. The formal consistency of the approach depends on a fuzzy semantic which easily generalizes most of the existing models, granting broad applicability to the suggested procedure. The applicability of an enhanced two-level Boolean minimizer is demonstrated, and the technique is applied to the fuzzy identification of nonlinear systems, consistently reducing the number of rules and easing application of further optimization interventions.

Optimization of fuzzy expert systems using genetic algorithms and neural networks
C. Perneel, J. M. Themlin, J. M. Renders, M. Acheroy
vol. 3, no. 3, pp. 300-12, Aug. 1995

In this paper, fuzzy logic theory is used to build a specific decision-making system for heuristic search algorithms. Such algorithms are typically used for expert systems. To improve the performance of the overall system, a set of important parameters of the decision-making system is identified. Two optimization methods for the learning of the optimum parameters, namely genetic algorithms and gradient-descent techniques based on a neural network formulation of the problem, are used to obtain an improvement of the performance. The decision-making system and both optimization methods are tested on a target recognition system.

Elicitation, assessment, and pooling of expert judgments using possibility theory
S. A. Sandri, D. Dubois, H. W. Kalfsbeek
vol. 3, no. 3, pp. 313-35, Aug. 1995

The problem of modeling expert knowledge about numerical parameters in the field of reliability is reconsidered in the framework of possibility theory. Usually expert opinions about quantities such as failure rates are modeled, assessed, and pooled in the setting of probability theory. This approach does not seem to always be natural since probabilistic information looks too rich to be currently supplied by individuals. Indeed, information supplied by individuals is often incomplete, imprecise rather than tainted with randomness. Moreover, the probabilistic framework looks somewhat restrictive to express the variety of possible pooling modes. In this paper, the authors formulate a model of expert opinion by means of possibility distributions that are thought to better reflect the imprecision pervading expert judgments. They are weak substitutes to unreachable subjective probabilities. Assessment evaluation is carried out in terms of calibration and level of precision, respectively, measured by membership grades and fuzzy cardinality indexes. Finally, drawing from previous works on data fusion using possibility theory, the authors present various pooling modes with their formal model under various assumptions concerning the experts. A comparative experiment between two computerized systems for expert opinion analysis has been carried out, and its results are presented in this paper.

Fuzzy control on the basis of equality relations with an example from idle speed control
F. Klawonn, J. Gebhardt, R. Kruse
vol. 3, no. 3, pp. 336-50, Aug. 1995

The way engineers use fuzzy control in real world applications is often not coherent with an understanding of the control rules as logical statements or implications. In most cases fuzzy control can be seen as an interpolation of a partially specified control function in a vague environment, which reflects the indistinguishability of measurements or control values. In this paper the authors show that equality relations turn out to be the natural way to represent such vague environments and they develop suitable interpolation methods to obtain a control function. As a special case of our approach the authors obtain Mamdani's model and can justify the inference mechanism in this model and the use of triangular membership functions not only for the reason of simplified computations, and they can explain why typical fuzzy partitions are preferred. The authors also obtain a criterion for reasonable defuzzification strategies. The fuzzy control methodology introduced in this paper has been applied successfully in a case study of engine idle speed control for the Volkswagen Golf GTI.

An application of fuzzy set theory for an electronic video camera image stabilizer
Y. Egusa, H. Akahori, A. Morimura, N. Wakami
vol. 3, no. 3, pp. 351-6, Aug. 1995

An electronic video camera image stabilizer has been developed which eliminates a substantial part of the image instability caused by the involuntary movement of camera holders. For the discrimination between image movement caused by unstable hand-holding and that of moving objects, fuzzy set theory is applied through the following process: 1) dividing the image taken by the camera into four regions, 2) providing two signals to discriminate between the causes of image instability, 3) evaluating these two discriminating signals after they are transformed into reliability values by membership functions, and 4) tuning the membership functions using a simplex method. This image stabilizer has been incorporated into a new compact video camera, and its substantially improved field performance has been confirmed.

Comparison of crisp and fuzzy character neural networks in handwritten word recognition
P. Gader, Mohamed Magdi, Hsien Chiang Jung
vol. 3, no. 3, pp. 357-63, Aug. 1995

Experiments comparing neural networks trained with crisp and fuzzy desired outputs are described. A handwritten word recognition algorithm using the neural networks for character level confidence assignment was tested on images of words taken from the United States Postal Service mailstream. The fuzzy outputs were defined using a fuzzy k-nearest neighbor algorithm. The crisp networks slightly outperformed the fuzzy networks at the character level but the fuzzy networks outperformed the crisp networks at the word level. This empirical result is interpreted as an example of the principle of least commitment.

An index of applicability for the decomposition method of multivariable fuzzy systems
P. G. Lee, K. K. Lee, G. J. Jeon
vol. 3, no. 3, pp. 364-9, Aug. 1995

Research on the application of fuzzy set theory to the design of control systems has led to interest in decomposition of multivariable fuzzy systems. Decomposition of multivariable control rules is preferable since it alleviates the complexity of the problem, but the inference error is inevitable because of its approximate nature. In this paper we show that a large inference error is generated when the Gupta's decomposition method (1986) is applied to Exclusive-nor (ENOR) gate model which is used as a counter example. We define an index of applicability which can classify whether the decomposition method can be applied to a multivariable fuzzy system or not.

On cluster validity for the fuzzy c-means model
N. R. Pal, J. C. Bezdek
vol. 3, no. 3, pp. 370-9, Aug. 1995

Many functionals have been proposed for validation of partitions of object data produced by the fuzzy c-means (FCM) clustering algorithm. We examine the role a subtle but important parameter-the weighting exponent m of the FCM model-plays in determining the validity of FCM partitions. The functionals considered are the partition coefficient and entropy indexes of Bezdek, the Xie-Beni (1991), and extended Xie-Beni indexes, and the Fukuyama-Sugeno index (1989). Limit analysis indicates, and numerical experiments confirm, that the Fukuyama-Sugeno index is sensitive to both high and low values of m and may be unreliable because of this. Of the indexes tested, the Xie-Beni index provided the best response over a wide range of choices for the number of clusters, (2-10), and for m from 1.01-7. Finally, our calculations suggest that the best choice for m is probably in the interval (1.5, 2.5), whose mean and midpoint, m=2, have often been the preferred choice for many users of FCM.