作者：
Zhu, XiaohuaWang, NingMinnan Normal Univ
Coll Phys & Informat Engn Zhangzhou 363000 Fujian Peoples R China Zhejiang Univ
State Key Lab Ind Control Technol Inst Cyber Syst & Control Hangzhou 310027 Zhejiang Peoples R China
Obtaining an accurate mathematical model is an important subject to design an overhead crane control system. However, there are some deviations between an existing model and a physical system due to its nonlinearity a...
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Obtaining an accurate mathematical model is an important subject to design an overhead crane control system. However, there are some deviations between an existing model and a physical system due to its nonlinearity and complexity characteristics. Motivated by this fact, an adaptive network-based fuzzy inference system (ANFIS) modeling method is proposed for obtaining high precision models. One of the challenges in ANFIS modeling is how to effectively optimize the premise and consequent parameters. To solve this problem, we propose the rna genetic algorithm with hairpin genetic operators (hrna-GA). In hrna-GA, inspired by the hairpin structure in rna molecules, we design the hairpin crossover operator and the hairpin mutation operator to maintain the population diversity and avoid the premature convergence. Numerical experiments have been conducted on some benchmark functions. The results indicate that hrna-GA has better search ability with respect to quality and stability of solutions. Finally, hrna-GA is applied to find the optimal parameters of ANFISs for modeling an actual overhead crane system and the satisfactory results are reached.
A density peaks clustering based on improved rna genetic algorithm (DPC-rnaGA) is proposed in this paper. To overcome the problems of Clustering by fast search and find of density peaks (referred to as DPC), DPC-rnaGA...
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ISBN:
(数字)9783319745213
ISBN:
(纸本)9783319745213;9783319745206
A density peaks clustering based on improved rna genetic algorithm (DPC-rnaGA) is proposed in this paper. To overcome the problems of Clustering by fast search and find of density peaks (referred to as DPC), DPC-rnaGA uses exponential method to calculate the local density, In addition, improved rna-GA was used to search the optimums of local density and distance. So clustering centers can be determined easily. Numerical experiments on synthetic and real-world datasets show that, DPC-rnaGA can achieve better or comparable performance on the benchmark of clustering, adjusted rand index (ARI), compared with K-means, DPC and Max_Min SD methods.
This paper proposes a hybrid type-2 fuzzy logic system architecture with the aid of rna genetic algorithm for a double inverted pendulum system. As an extension of type-1 fuzzy logic system, type-2 fuzzy logic system ...
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This paper proposes a hybrid type-2 fuzzy logic system architecture with the aid of rna genetic algorithm for a double inverted pendulum system. As an extension of type-1 fuzzy logic system, type-2 fuzzy logic system can effectively improve the control performance by uncertainty of membership function especially when we have to confront with corrupted data or unpredicted external disturbances. In this proposed method, the related parameters of type-1 and type-2 fuzzy logic systems are respectively optimized by using rna genetic algorithm. Through computer simulation and comparisons, the better performance can be achieved by using optimized type-2 fuzzy logic system with rna genetic algorithm. (C) 2014 Elsevier Inc. All rights reserved.
Inspired by the biological rna, a circular genetic operators based rna genetic algorithm (crna-GA) is proposed to estimate the model parameters of the proton exchange membrane fuel cell (PEMFC). To maintain the popula...
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Inspired by the biological rna, a circular genetic operators based rna genetic algorithm (crna-GA) is proposed to estimate the model parameters of the proton exchange membrane fuel cell (PEMFC). To maintain the population diversity and avoid premature convergence, we design the novel genetic operator of the double-loop crossover operator. To allow the algorithm to jump out of local optima, the adaptive mutation probabilities are presented and the stem-loop mutation operator is adopted with the other mutation operators. The simulated annealing method is also incorporated into the crna-GA to improve local search ability. Performance tests conducted on some typical benchmark functions have witnessed the validity of crna-GA. The crna-GA is also applied to estimate the parameters of the PEMFC model and the satisfactory results have shown its effectiveness. Copyright (C) 2014, Hydrogen Energy Publications, LLC. Published by Elsevier Ltd. All rights reserved.
Accurate model plays an important role in designing, assessing, and controlling photovoltaic (PV) systems. In this work, the least-squares support vector machine (LSSVM) is adopted to model the current-voltage (V-I) c...
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Accurate model plays an important role in designing, assessing, and controlling photovoltaic (PV) systems. In this work, the least-squares support vector machine (LSSVM) is adopted to model the current-voltage (V-I) characteristic curves of different PV systems. A novel rna genetic algorithm (bvrna-GA) is proposed to determine the parameters of LSSVM. The bvrna-GA is featured by designing the bulge loop crossover operator and the virus-induced mutation operator, they are employed to balance the exploration and exploitation capacities. Different experiments with 10 benchmark functions are conducted to show that the search efficiency of bvrna-GA is better than the other four state-of-art algorithms. The outputs of bvrna-GA optimized LSSVM models can better agree with the real outputs of different PV systems, the modeling results demonstrate the effectiveness of bvrna-GA in solving real-world problems. (C) 2021 Elsevier B.V. All rights reserved.
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