Journal of Intelligent & Fuzzy Systems - Volume 17, issue 2

Authors: Juang, Chia-Feng

Article Type: Research Article

Abstract: A new approach that automates the design of Fuzzy systems by hybridizing Multi-group Genetic Algorithm and Particle Swarm Optimization, called F-MGAPSO, is proposed in this paper. By F-MGAPSO, we aim to simultaneously design the number of fuzzy rules and free parameters in a fuzzy system. In the initial population of the conceived GA model, the number of rules encoded in each individual is randomly assigned, and the individuals with equal number of rules constitute the same …group. These groups will compete against with each other, and the superior ones will gradually prevail over inferiors. Evolution of population consists of three major operations: group enhancement, variable-length individual crossover and mutation. Group enhancement is to enhance elites in each group by a local-neighborhood version of particle swarm optimization. By performing variable-length individual crossover and mutation operations on elites of the same or different groups, we create offsprings, and non-elites in the old population are replaced by these newly bred ones. Performance of F-MGAPSO is verified through simulations and comparisons with other types of genetic algorithms. Show more

Keywords: Flexible partition, structure/parameter learning, TSK-type fuzzy rule, fuzzy control, rapid thermal processing control

Citation: Journal of Intelligent & Fuzzy Systems, vol. 17, no. 2, pp. 83-93, 2006

Authors: Chen, Chung-Cheng | Wu, Tzung-Han | Chen, Ying-Jen

Article Type: Research Article

Abstract: This paper first studies the output tracking and almost disturbance decoupling problem of nonlinear control systems with mismatch uncertainties based on the feedback linearization approach and fuzzy logic control. The main contribution of this study is to construct a composite controller, under appropriate conditions, such that the resulting closed-loop system is valid for any initial condition and bounded tracking signal with the following characteristics: input-to-state stability with respect to disturbance inputs and …almost disturbance decoupling, i.e., the influence of disturbances on the L_2 norm of the output tracking error can be arbitrarily attenuated by increasing some adjustable parameters. One example, which cannot be solved by previous papers on the almost disturbance decoupling problem, is proposed in this paper to exploit the fact that the tracking and the almost disturbance decoupling performances are easily achieved by the proposed approach. In order to demonstrate the practical applicability, this paper has investigated an inverted pendulum control system. Show more

Keywords: Takagi-Sugeno fuzzy model, fuzzy logic control, almost disturbance decoupling, feedback linearization approach, uniform ultimate bounded, composite Lyapunov approach

Citation: Journal of Intelligent & Fuzzy Systems, vol. 17, no. 2, pp. 95-111, 2006

Authors: Kucukdemiral, Ibrahim B. | Cansever, Galip

Article Type: Research Article

Abstract: A robust adaptive Sugeno based fuzzy sliding mode controller equipped with a supervisory controller is considered for the control of nonlinear systems which significantly reduce computation via reducing the size of fuzzy rule base. Besides, all the system parameters such as control and state signals can be confined to a predefined bounded region by use of proposed control method. Also the proposed method does not need any information of the controlled plant. Tracking and regulation simulations …on an inverted pendulum system highlights that the proposed method is superior than the classical Sugeno based fuzzy controllers for the control of ill-defined nonlinear systems. Show more

Citation: Journal of Intelligent & Fuzzy Systems, vol. 17, no. 2, pp. 113-124, 2006

Authors: Fanaei, Arash | Farrokhi, Mohammad

Article Type: Research Article

Abstract: In this paper, an adaptive control method for hybrid position/force control of robot manipulators, based on neuro-fuzzy modeling, is presented. Since force control involves applying certain amount of force on the surface of an object, it is important to consider the friction force between end-effector and surface into account. In order to compensate this friction force, a robust and adaptive neuro-fuzzy compensator will be designed and incorporated into the close-loop system. Moreover, to determine stiffness coefficient …of surface, an on-line estimator will be designed for more precise computation of the desired force. Due to the adaptive neuro-fuzzy modeling, the proposed controller is independent of robot dynamics, since the free parameters of the neuro-fuzzy controller are adaptively updated to cope with changes in the system and the environment. As a result, the tracking error, both for position and force, will always remain small. Also, the stability of the controller is guaranteed, since the adaptation law is based on Lyapunov theory. In addition to that, the convergence of the adaptive parameters will be proved in this paper. The simulation results show good performance of the proposed controller as compared with other conventional control schemes for robot manipulators such as computed torque method. Show more

Keywords: Robot, hybrid force/position control, adaptive control, neuro-fuzzy control, surface friction compensator

Citation: Journal of Intelligent & Fuzzy Systems, vol. 17, no. 2, pp. 125-144, 2006

Authors: Lin, Cheng-Jian | Lee, Chi-Yung | Chin, Cheng-Chung

Article Type: Research Article

Abstract: This paper addresses a Compensatory Wavelet Neuro-Fuzzy System (CWNFS) for temperature control. The proposed CWNFS model is five-layer structure, which combines the traditional Takagi-Sugeno-Kang (TSK) fuzzy model and the wavelet neural networks (WNN). We adopt the non-orthogonal and compactly supported functions as wavelet neural network bases. Besides, the compensatory fuzzy reasoning method is used in adaptive fuzzy operations that can make the fuzzy logic system more adaptive and effective. An on-line learning …algorithm, which consists of structure learning and parameter learning, is presented. The structure learning is based on the degree measure to determine the number of fuzzy rules and wavelet functions. The parameter learning is based on the gradient descent method to adjust the shape of membership function, compensatory operations and the connection weights of WNN. Simulation results have been given to illustrate the performance and effectiveness of the proposed model. Show more

Keywords: Temperature control, TSK-type fuzzy model, wavelet neural networks, on-line learning, gradient descent, compensatory operation

Citation: Journal of Intelligent & Fuzzy Systems, vol. 17, no. 2, pp. 145-157, 2006

Authors: Mosavi, M.R.

Article Type: Research Article

Abstract: The ability to determine an accurate global position has many useful commercial and military applications. Because of the L1 GPS receiver's error sources, it is essential to model them. In this paper, a new approach is presented for improving low cost receivers positioning accuracy with Differential GPS (DGPS) corrections real time prediction using pi-sigma, sigma-pi, recurrent, and parallel recurrent neural networks. Methods validity is verified with experimental data from an actual data collection, before and …after Selective Availability (SA) error. The result is a highly effective estimation technique for accurate real time positioning; so that prediction RMS errors were less than 0.40 meter after prediction, independent of SA error. The experimental test results with real data emphasize that total performance of RNN is better than PSNN and SPNN considering trade off between accuracy and speed for DGPS corrections prediction. The performance of proposed Parallel Recurrent Neural Network (PRNN) is compared with RNN in DGPS corrections real time prediction. The experimental results demonstrate which the PRNN has great approximation ability and suitability than RNN; so that the PRNN prediction total RMS error respect to the RNN is improved from 2.7348 to 1.7576 meters for 10 seconds ahead prediction and from 4.0397 to 2.5937 meters for 30 second ahead prediction, respectively. Show more

Citation: Journal of Intelligent & Fuzzy Systems, vol. 17, no. 2, pp. 159-171, 2006

Authors: Torkul, O. | Cedimoglu, I.H. | Geyik, A.K.

Article Type: Research Article

Abstract: There are several methods and techniques for manufacturing cell design. Many clustering techniques have focused on operations that have been made on part-machine matrix. Some methods have used array-based techniques while others have used similarity coefficient or distance criteria in order to determine clusters. It has been seen from the recent researches, solutions may be changeable related to using algorithms. In order to investigate the applicability of several techniques it is necessary to obtain in comparison …with each other. Artificial intelligence technologies are commonly in use in clustering problems as other manufacturing issues. In this study, fuzzy logic approach is studied in design of part families and machine cells simultaneously. The aim at this study is to compare manufacturing cell design which made of fuzzy clustering algorithm (Fuzzy C-Means) with the crisp methods. It has been seen from the result of the study, fuzzy clustering solutions may be efficient than the crisp method for the selected data sets. Show more

Keywords: Fuzzy clustering, cell formation, cellular manufacturing

Citation: Journal of Intelligent & Fuzzy Systems, vol. 17, no. 2, pp. 173-181, 2006

Authors: Torabi, H. | Davvaz, B. | Behboodian, J.

Article Type: Research Article

Abstract: Fuzzy set theory has been well developed and applied in a wide variety of real problems. In some probabilistic problems, there does not exist complete information about the probability model. In this paper, using fuzzy rough set theory, we obtain a lower and upper probability for an arbitrary fuzzy random event and then we introduce a measure for inclusivity of fuzzy events.

Keywords: Rough set, fuzzy set, probability space, random variable

Citation: Journal of Intelligent & Fuzzy Systems, vol. 17, no. 2, pp. 183-188, 2006