Journal of Intelligent & Fuzzy Systems - Volume 2, issue 1

Authors: Lee, Minho | Lee, Soo-Young | Park, Cheol Hoom

Article Type: Research Article

Abstract: A neuro-fuzzy identifier for fuzzy modeling of a system is explained, and a control structure using this neurofuzzy identifier is proposed. The neuro-fuzzy identifier contains not only an adaptive clustering process for determining center points of the input and virtual output membership functions but also an adaptive process for deciding the shapes of the input membership functions. Moreover, linguistic fuzzy rules of a system can be obtained from the proposed neuro-fuzzy identifier, which can learn the initial implication fuzzy control rules of the system and then compensate for the error of the initial fuzzy control rules by a feedback control …structure that maintains the stability of the system. Computer simulation shows that neuro-fuzzy identification is very effective in modeling fuzzy systems the fuzzy rules of which cannot be obtained easily and the neuro-fuzzy controller gives very effective control results by a learning process. Show more

DOI: 10.3233/IFS-1994-2101

Citation: Journal of Intelligent and Fuzzy Systems, vol. 2, no. 1, pp. 1-14, 1994

Authors: Rodríguez-Vazquez, A. | Delgado-Restituto, M.

Article Type: Research Article

Abstract: This article presents architectures and circuit techniques to design chaotic piecewise-linear discrete maps in CMOS VLSI. Maps are presented for white and 1/fγ noise generation, as well as to display bidimensional strange attractors. The proposed circuits use only MOS transistors and thus are suitable for implementation in standard CMOS technologies containing only one poly layer. A family of CMOS building blocks for piece-wise-linear function interpolation in current-domain is presented based upon the use of a current switch. Novel circuit strategies are given for improved current-switch performance as well for high-resolution current comparison. These strategies can be used for the …design of chaotic current-mode circuits, as well for the representation problem via basis functions in feedforward neural networks and fuzzy inference engines. Also, an innovative scheme for adaptive feedthrough cancellation in dynamic current mirrors is presented, which aids in reducing the most significant error source in chaotic current-mode discrete maps. The article includes experimental results from monolithic n-well CMOS 1.6 μm prototypes of the building blocks as well as complete systems; in particular, a chaotic neuron according to the Aihara's model and a broadband white noise generator. Show more

DOI: 10.3233/IFS-1994-2102

Citation: Journal of Intelligent and Fuzzy Systems, vol. 2, no. 1, pp. 15-37, 1994

Authors: Yager, Ronald R. | Filev, Dimitar P.

Article Type: Research Article

Abstract: A methodology is suggested here for the development of fuzzy systems models that is a combination of the AI-expert systems approach, with its heavy dependence on the use of expert knowledge, and the neural network-type systems building, with its emphasis on learning from data observations. We use expert-provided information in the form of template linguistic values to induce potential elemental rules for the knowledge base of the system model. We then introduce input-output observations into a simple learning mechanism to obtain weights characterizing the effect of each of the potential elemental rules on the overall systems model. The development of …the learning mechanism is based on a representation of systems models combining fuzzy logic and Dempster-Shafer theory, which we previously introduced. Show more

DOI: 10.3233/IFS-1994-2103

Citation: Journal of Intelligent and Fuzzy Systems, vol. 2, no. 1, pp. 39-54, 1994

Authors: Melhem, Hani | Aturaliya, Senaka

Article Type: Research Article

Abstract: This article summarizes several attempts made to apply fuzzy logic to structural engineering and particularly to bridge engineering. Many of these attempts have been implemented in depth and are readily available for practical use. The major research activity has been in the areas of structural damage assessment, bridge condition evaluation, and structural rating. In the case of bridge rating, the inspection procedure currently being used suggested the need for a multi-attributive decision making model. Two fuzzy models previously proposed in the literature and a third model adopted by the authors are presented here. The later produces a priority setting obtained …through the solution of an eigenvalue problem involving a closest discrete pairwise matrix indicative of the relative structural importance of the different elements of the bridge components considered. Show more

DOI: 10.3233/IFS-1994-2104

Citation: Journal of Intelligent and Fuzzy Systems, vol. 2, no. 1, pp. 55-68, 1994

Authors: Iwahori, Yuji | Ishii, Naobiro | Woodham, Robert J. | Ozaki, Masahiro | Adachi, Yoshinori

Article Type: Research Article

Abstract: This article proposes a new idea to determine surface gradients uniquely by using neural networks that can learn any reflectance maps. The Phong illuminating function is used to represent the glossy surface, including Lambertian surfaces, and it includes three parameters that characterize the reflectance property of the object susrface. This article shows that Phong reflectance functions from three different directions can be learned through neural networks by treating the values of three image irradiances as inputs while treating the corresponding two surface gradient parameters as outputs. Computer simulation was demonstrated for three layered networks. Learning was done for a spherical …object and it was repeated by the back-propagation algorithm. The desirable surface gradients could be recovered by neural networks when any triples of image irradiances were inputed as a test pattern. Neural networks have a great capacity to store the three reflectance maps, and this method offers the advantage that special analysis to solve the simultaneous equations is not required as in the conventional method. Show more

DOI: 10.3233/IFS-1994-2105

Citation: Journal of Intelligent and Fuzzy Systems, vol. 2, no. 1, pp. 69-73, 1994

Authors: de Korvin, A. | Bourgeois, B. | Kleyle, R.

Article Type: Research Article

Abstract: In this article we consider attributes to be fuzzy sets. Knowledge acquisition takes place by looking at examples. In each example, attributes as well as a corresponding decision is made available. The decision may be a fuzzy diagnosis. Based on these examples two sets of fuzzy rules are constructed: certain rules and possible rules. Corresponding measures of how much we believe these rules are also constructed. The concept of how much a fuzzy diagnosis is definable in terms of fuzzy attributes is studied. Finally, classifications and some of their properties are analyzed.

DOI: 10.3233/IFS-1994-2106

Citation: Journal of Intelligent and Fuzzy Systems, vol. 2, no. 1, pp. 75-87, 1994

Authors: Hambaba, M. | Elsherif, H. | O'Brien, W. | Bergstrom, E.

Article Type: Research Article

Abstract: A framework for using artificial neural networks to design injection molded parts is presented. This framework can be achieved through two approaches. The first approach uses a mapping technique to represent the design knowledge into an artificial neural network. This mapping approach is used in a distributed way to encode the design knowledge. The second approach uses an intelligent hybrid system that integrates the knowledge-base (expert) system and the neural networks together into one combined system. This tightly coupled intelligent system takes the benefits of the expert system and the neural networks and combines them to eliminate the limitations of …each. Working design examples of these two implemented approaches will be explored. The concepts to be incorporated in future implementations will be mentioned. Show more

DOI: 10.3233/IFS-1994-2107

Citation: Journal of Intelligent and Fuzzy Systems, vol. 2, no. 1, pp. 89-97, 1994

Authors: Langari, Reza

Article Type: Book Review

DOI: 10.3233/IFS-1994-2108

Citation: Journal of Intelligent and Fuzzy Systems, vol. 2, no. 1, pp. 99-99, 1994