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29
served as the EditorInChief for the
 IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY from
, 1997
"... High American PressScientia Magna is published annually in 400500 pages per volume and 1,000 copies. ..."
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High American PressScientia Magna is published annually in 400500 pages per volume and 1,000 copies.
Benchmark Antenna Problems for Evolutionary Optimization Algorithms
"... Abstract—A set of antennaoptimization problems is presented that satisfies the necessary requirements to form a test suite useful for measuring and comparing the performance of different evolutionary optimization algorithms (EAs) when they are applied to solve complex electromagnetic problems. The ..."
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Abstract—A set of antennaoptimization problems is presented that satisfies the necessary requirements to form a test suite useful for measuring and comparing the performance of different evolutionary optimization algorithms (EAs) when they are applied to solve complex electromagnetic problems. The ability of the proposed test suite to find strong and weak points of any EA is illustrated by a complete study of four broadly used evolutionary algorithms carried out with the aid of the new test functions. Index Terms—Antennas, genetic algorithms (GAs), optimization methods, particle swarm. I.
Genetic Algorithms with Automatic Accelerated Termination
"... The standard versions of Evolutionary Algorithms (EAs) have two main drawbacks: unlearned termination criteria and slow convergence. Although several attempts have been made to modify the original versions of Evolutionary Algorithms (EAs), only very few of them have considered the issue of their ter ..."
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The standard versions of Evolutionary Algorithms (EAs) have two main drawbacks: unlearned termination criteria and slow convergence. Although several attempts have been made to modify the original versions of Evolutionary Algorithms (EAs), only very few of them have considered the issue of their termination criteria. In general, EAs are not learned with automatic termination criteria, and they cannot decide when or where they can terminate. On the other hand, there are several successful modifications of EAs to overcome their slow convergence. One of the most effective modifications is Memetic Algorithms. In this paper, we modify genetic algorithm (GA), as an example of EAs, with new termination criteria and acceleration elements. The proposed method is called GA with Automatic Accelerated Termination (G3AT). In the G3AT method, Gene Matrix (GM) is constructed to equip the search process with a selfcheck to judge how much exploration has been done. Moreover, a special mutation operation called “Mutagenesis ” is defined to achieve more efficient and faster exploration and exploitation processes. The computational experiments show the efficiency of the G3AT method, especially the proposed termination criteria.
OBSERVABILITY ROBUSTNESS OF UNCERTAIN FUZZYMODELBASED CONTROL SYSTEMS
"... Abstract. The problem considered in this study is the observability robustness of Takagi Sugeno (TS) fuzzymodelbased control systems. Where a nominal TSfuzzymodelbased control system is locally observable (i.e., where each fuzzy rule in the system has a full row rank for its observability mat ..."
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Abstract. The problem considered in this study is the observability robustness of Takagi Sugeno (TS) fuzzymodelbased control systems. Where a nominal TSfuzzymodelbased control system is locally observable (i.e., where each fuzzy rule in the system has a full row rank for its observability matrix), a sufficient condition is proposed to preserve the assumed property when system uncertainties are considered. The proposed sufficient condition preserves the assumed property by indicating the explicit relationships of bounds on system uncertainties. A robustly global observability condition is also presented for uncertain TSfuzzymodelbased control systems. Finally, the proposed sufficient conditions are applied in the example of a nonlinear massspringdamper mechanical system with system uncertainties. Keywords: Fuzzy system models, Fuzzy control, Robust observability, TakagiSugeno (TS) fuzzy model, System uncertainties 1. Introduction. The fuzzymodelbased representation proposed by Takagi and Sugeno [1], known as the TS fuzzy model, has proven effective in many nonlinear control systems ([28] and references therein). The robust controllability of the uncertain TSfuzzymodelbased control systems has also been studied by Chen et al. In practice, however, obtaining accurate values may be difficult, if not impossible, for some system parameters due to inaccurate measurements or due to inaccessible or variable system parameters and sensor and actuator positions. These system uncertainties may negate the observability property of the TSfuzzymodelbased control systems. The
Europe
"... Multiobjective optimization problems with many local Pareto fronts is a big challenge to evolutionary algorithms. In this paper, two operators, biased initialization and biased crossover, are proposed to improve the global search ability of RMMEDA, a recently proposed multiobjective estimation of d ..."
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Multiobjective optimization problems with many local Pareto fronts is a big challenge to evolutionary algorithms. In this paper, two operators, biased initialization and biased crossover, are proposed to improve the global search ability of RMMEDA, a recently proposed multiobjective estimation of distribution algorithm. Biased initialization inserts several globally Pareto optimal solutions into the initial population; biased crossover combines the location information of some best solutions found so far and globally statistical information extracted from current population. Experiments have been conducted to study the effects of these two operators.
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and in the United Kingdom by Information Science Reference (an imprint of IGI Global)
Learning of a Singlehidden Layer Feedforward Neural Network using an Optimized Extreme Learning Machine
"... This paper proposes a learning framework for singlehidden layer feedforward neural networks (SLFN) called optimized extreme learning machine (OELM). In OELM, the structure and the parameters of the SLFN are determined using an optimization method. The output weights, like in the batch ELM, are o ..."
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This paper proposes a learning framework for singlehidden layer feedforward neural networks (SLFN) called optimized extreme learning machine (OELM). In OELM, the structure and the parameters of the SLFN are determined using an optimization method. The output weights, like in the batch ELM, are obtained by a least squares algorithm, but using Tikhonov’s regularization in order to improve the SLFN performance in the presence of noisy data. The optimization method is used to select the set of input variables, the hiddenlayer configuration and bias, the input weights and the Tikhonov’s regularization factor. The proposed framework has been tested with three optimization methods (genetic algorithms, simulated annealing, and differential evolution) over sixteen benchmark problems available in public repositories.
1Institute of Systems and Robotics (ISRUC),
"... This paper proposes a learning algorithm for singlehidden layer feedforward neural networks (SLFN) called genetically optimized extreme learning machine (GOELM). In the GOELM, the structure and the parameters of the SLFN are optimized by a genetic algorithm (GA). The output weights, like in the b ..."
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This paper proposes a learning algorithm for singlehidden layer feedforward neural networks (SLFN) called genetically optimized extreme learning machine (GOELM). In the GOELM, the structure and the parameters of the SLFN are optimized by a genetic algorithm (GA). The output weights, like in the batch ELM, are obtained by a least squares algorithm, but using Tikhonov’s regularization in order to improve the SLFN performance in the presence of noisy data. The GA is used to tune the set of input variables, the hiddenlayer configuration and bias, the input weights and the Tikhonov’s regularization factor. The proposed method was applied and compared with four other methods over five benchmark problems available in a public repository. Besides it was applied in the estimation of the temperature at the burning zone of a real cement kiln plant. 1
Research Article Hybrid TaguchiDifferential Evolution Algorithm for Parameter Estimation of Differential Equation Models with Application to HIV Dynamics
, 2010
"... the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. This work emphasizes solving the problem of parameter estimation for a human immunodeficiency virus HIV dynamical model by using an ..."
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the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. This work emphasizes solving the problem of parameter estimation for a human immunodeficiency virus HIV dynamical model by using an improved differential evolution, which is called the hybrid Taguchidifferential evolution HTDE. The HTDE, used to estimate parameters of an HIV dynamical model, can provide robust optimal solutions. In this work, the HTDE approach is effectively applied to solve the problem of parameter estimation for an HIV dynamical model and is also compared with the traditional differential evolution DE approach and the numerical methods presented in the literature. An illustrative example shows that the proposed HTDE gives an effective and robust way for obtaining optimal solution, and can get better results than the traditional DE approach and the numerical methods presented in the literature for an HIV dynamical model. 1.