Journal of Intelligent & Fuzzy Systems - Volume 7, issue 4

Authors: Concha, Julio | Cipriano, Aldo

Article Type: Research Article

Abstract: Fuzzy systems have been successfully applied to the design of knowledge based controllers, yielding very good performance in many cases. However, fuzzy control still lacks general formal analysis and design techniques that allow the designer ensure a priori certain features of the closed-loop system, particularly stability. This article presents a simple systematic design procedure, based on approximate linearization, that guarantees closed-loop asymptotic stability. The method is applicable when a (nonfuzzy) plant model is available and a Takagi–Sugeno fuzzy controller is to be designed. The proposed design method is illustrated with an example and simulation results.

Citation: Journal of Intelligent and Fuzzy Systems, vol. 7, no. 4, pp. 325-333, 1999

Authors: Hong, Sung Kyung | Langari, Reza

Article Type: Research Article

Abstract: This paper deals with the control of a levitated object with position dependent nonlinearity. We applied the fuzzy modeling and control to a nonlinear magnetic bearing system to obtain uniform desired performance over the entire clearance. We represented the nonlinear magnetic bearing by the Takagi–Sugeno–Kang (TSK) fuzzy model, where a nonlinear global model is approximated by a set of linear local models. Then a model-based fuzzy controller, the so-called parallel distributed compensation (PDC), is employed. Simulation and experimentation results demonstrate that the controller provides excellent compensation for unstable/nonlinear characteristics of the magnetic bearing system and maximizes the controller's valid region …of operation, while guaranteeing pre-specified transient performance. Show more

Citation: Journal of Intelligent and Fuzzy Systems, vol. 7, no. 4, pp. 335-346, 1999

Authors: Ganjavi, M.R. | Lucas, C. | Javidi, M.H.

Article Type: Research Article

Abstract: In this paper the linguistic approximate reasoning method is used for short term load forecasting. A neural structure for inference processing units is put forward. Two different — Analogical and Deductive — approaches to the inference method have been distinguished. Correspondingly, two different architectures — Analogical and Deductive fuzzy neural networks — are introduced in this paper. The accuracy of the load forecasting, as well as the size of the required rule base have been compared for Analogical, Deductive, and conventional fuzzy neural networks. It is shown that Analogical and Deductive approaches have superior performance in this application.

Citation: Journal of Intelligent and Fuzzy Systems, vol. 7, no. 4, pp. 347-357, 1999

Authors: Hsu, Wynne | Lee, C.S.G.

Article Type: Research Article

Abstract: It has long been recognized that we need some fundamental, correct principles and methodologies to guide decision making in design. One important decision to be made in design is the determination of the optimal degree of design coupling among the components. Two components are physically coupled together if the design features of both components have been integrated to form one single physical component. This ability to decide on the optimal degree of design coupling has a direct impact on the number of components in the final design which, in turns, affects the maintainability, assemblability, testability, and adaptability of the resultant …product. Design for assembly guidelines tell us to reduce the number of components in the final design. This implies that we should, as far as possible, strive for the highest degree of physical integration of parts. This may prove to be too simplistic a guideline. In this paper, we attempt to show that there exist circumstances where design coupling is undesirable. We then propose a systematic approach for determining the optimal degree of design coupling among the components. Show more

Citation: Journal of Intelligent and Fuzzy Systems, vol. 7, no. 4, pp. 359-372, 1999

Authors: Flood, Ian

Article Type: Research Article

Abstract: The paper proposes and evaluates an artificial neural network based method of modeling the dynamic behavior of continua. The technique is applicable to situations where the differential equations governing the behavior of a system are nonlinear and poorly understood, and the data available for training is noisy. A method of modeling the unknown component of governing differential equations using neural network technology, is first described. This includes a method for averaging out localized errors in the neural network function that results from noise in the training data. A description is then given of a radial-Gaussian neural network architecture and training …algorithm adopted for this application. The construction of a complete simulation model of a specific system from the trained neural networks is demonstrated. The performance of the proposed approach is assessed in a series of experiments simulating the nonlinear thermal behavior of a translucent solid material. The system is proven to perform most effectively using the proposed error averaging technique, and to be capable of providing an accurate simulation of a system's behavior sustained over many thousands of simulation time steps. Show more

Citation: Journal of Intelligent and Fuzzy Systems, vol. 7, no. 4, pp. 373-385, 1999

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

Article Type: Research Article

Abstract: Many problems in decision making structure reality into constituent parts, that is, into hierarchial charts in which the goal is at the top, while the decisions are at the lowest level of the chart. At the intermediate levels of the chart are the various attributes and/or conditions which must be considered in order to arrive at a decision. A rather simplified hierarchial chart is given, in which the goal, all attributes and all possible decisions occupy boxes in the hierarchial structure. The main idea in the Analytical Hierarchial Process (AHP) approach is to construct a pairwise ranking of the boxes …at any given level relative to the boxes at the next highest level to which they are connected. These pairwise rankings are used to construct priorities which are then combined to create an overall priority for each course of action under consideration. The course of action with the highest priority is then chosen. Uncertainty in the assigning of priorities and the use of semantic variables in their assignment lead naturally to the inclusion of fuzzy logic into the structure of the AHP paradigm. In this paper we propose a method for using fuzzy sets in the context of the Analytical Hierarchial approach to decision making. A rather comprehensive example illustrates this method. Show more

Citation: Journal of Intelligent and Fuzzy Systems, vol. 7, no. 4, pp. 387-400, 1999

Authors: Yager, Ronald R. | Kelman, Antoine

Article Type: Research Article

Abstract: The Analytical Hierarchy Process (AHP) provides a comprehensive methodology for the solution of multicriteria decision problems which makes considerable use of comparison generated importances to help in the aggregation of lower order concepts in the formulation of higher order concepts. We introduce an extension of the Analytical Hierarchy Process using the Ordered Weighted Averaging (OWA) operators. This extension, which generalizes the aggregation process used in the AHP, allows more flexibility in the formulation of higher order concepts and provides the AHP an even greater facility for modeling human decision making. Using the OWA operators we are able to model situations …where the number of sub-criteria needed to satisfy a higher order concept can be expressed in terms of linguistic quantifiers. Show more

Citation: Journal of Intelligent and Fuzzy Systems, vol. 7, no. 4, pp. 401-417, 1999

Authors: Hambaba, Mohamed L. | Jang, Wook-Jin

Article Type: Research Article

Abstract: In asynchronous transfer mode (ATM), the traffic cells from various information sources are statistically multiplexed at the physical layer to efficiently utilize the network resources. An ATM switching node must successfully route the many arriving traffic cells to the correct output without collisions of cells in the switch fabrics while sustaining the user-required quality of services (QoS) for all application. For scheduling of cells in a switch, neural networks are noted for their ability to process large amounts of data quickly using a copious number of highly interconnected processors. In this paper, we propose an optimal cell scheduling algorithm for …ATM switch using Hopfield neural network. The proposed algorithm finds a set of nonblocking cells with the ideal energy functions for Hopfield neural networks and efficiently minimizes the cell-delay time using a delay matrix scheme. Every cell transmission time, the algorithm gives the optimal scheduling which minimizes cell-loss and cell-delay time in buffer, and also solves the cell blocking and cell-sequence problems. Show more

Citation: Journal of Intelligent and Fuzzy Systems, vol. 7, no. 4, pp. 419-429, 1999