Magnetic levitation system is inherently unstable and strongly nonlinear in nature. Fixed optimal gain controllers designed at some nominal operating conditions fail to provide the best control performance over a wide...
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Magnetic levitation system is inherently unstable and strongly nonlinear in nature. Fixed optimal gain controllers designed at some nominal operating conditions fail to provide the best control performance over a wide range of off-nominal operating conditions. In this paper, an adaptive fuzzy parameter scheduling scheme for gravitational search algorithm (GSA) based optimal Proportional Integral Derivative (PID) and Lag-Lead controllers has been proposed to control a single actuator based DC Attraction type Levitation System (DCALS). A Takagi-Sugeno (T-S) fuzzy inference system is used in the proposed controllers. The inference system is extremely well suited to the task of smoothly interpolating linear gains across the input space when a strongly non-linear DCALS moves around in its operating space. Simulation results show that both proposed adaptive fuzzy PID and Lag-Lead controllers offer better performance than fixed gain controllers at different operating conditions.
In this paper, a new algorithm for image edge detection using gravitational search algorithm (GSA) is proposed. In order to adapt the proposed algorithm to edge detection problem, some modification is applied on the o...
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ISBN:
(纸本)9781467320733
In this paper, a new algorithm for image edge detection using gravitational search algorithm (GSA) is proposed. In order to adapt the proposed algorithm to edge detection problem, some modification is applied on the original GSA. Each image pixel is considered as a celestial body and its mass is considered to be corresponding to the pixel's grayscale intensity. To find out the edgy pixels a number of agents are randomly generated and initialized through the image space. Artificial agents move through the space via forces of bodies that are located in their neighborhood. A large number of experiments are employed to determine suitable algorithm parameters and confirm the legitimacy of the proposed algorithm.
With the continuous progression of semiconductor technology, nanoscale effects have become a persistent issue in the design of analog/mixed-signal (AMS) circuits. The cost of exploration and optimization of the design...
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ISBN:
(纸本)9780769547671
With the continuous progression of semiconductor technology, nanoscale effects have become a persistent issue in the design of analog/mixed-signal (AMS) circuits. The cost of exploration and optimization of the design space increases to infeasible levels with conventional design methodologies. Different modeling techniques to reduce the cost of design exploration, while ensuring the accuracy of such models, have been introduced and continue to be a research problem. In this paper, a geostatistical inspired metamodeling and optimization technique is presented for fast and accurate design optimization of nano-CMOS circuits. The proposed design methodology incorporates a simple Kriging based metamodel which efficiently and accurately predicts design performance. The metamodel (instead of the circuit netlist) is subjected to a gravitational search algorithm for optimization. This design methodology is applicable to AMS circuits and is illustrated with the optimization of power consumption of a 45nm CMOS thermal sensor. The method improves the power performance of the thermal sensor by 36.9% while reducing the design optimization time by 90% even with 6 design parameters.
An intelligent gravitational search algorithm (IGSA) is introduced to develop a novel classifier. The proposed method is called IGSA-classifier. At first, a fuzzy controller is designed for intelligently controlling t...
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ISBN:
(纸本)9781467357135;9781467357128
An intelligent gravitational search algorithm (IGSA) is introduced to develop a novel classifier. The proposed method is called IGSA-classifier. At first, a fuzzy controller is designed for intelligently controlling the effective parameters of GSA. Those are gravitational coefficient and the number of effective objects, two important parameters which play major roles on search process of GSA. Then the designed intelligent GSA is employed to construct a novel decision function estimation algorithm from feature space. Extensive experimental results on different benchmarks and a practical pattern recognition problem with nonlinear, overlapping class boundaries and different feature space dimensions are provided to show the capability of the proposed method. The comparative results show that the performance of the proposed classifier is comparable to or better than the performance of other swarm intelligence based and evolutionary classifiers.
The traditional gravitational search algorithm (GSA) has the advantages of easy implementation, fast convergence and low computational cost. However, GSA driven by the gravity law is easy to fall into local optimum so...
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ISBN:
(纸本)9783642247279
The traditional gravitational search algorithm (GSA) has the advantages of easy implementation, fast convergence and low computational cost. However, GSA driven by the gravity law is easy to fall into local optimum solution. The convergence speed slows down in the later search stage, and the solution precision is not good. Inspired by the biological immune system, we introduce the characteristics of antibody diversity and vaccination, and propose a novel immune gravitation optimization algorithm (IGOA) to help speed the convergence of evolutionary algorithms and improve the optimization capability. The comparison experiments of IGOA, GSA and PSO on some benchmark functions are carried out. The proposed algorithm shows competitive results with improved diversity and convergence. It provides new opportunities for solving previously intractable function optimization problems.
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