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This paper is devoted to designing higher-order adaptive PID controllers as a new generation of PID controllers for chaos synchronization, in which second order integration and second-order derivative terms to the PID controller (PII2DD2) are employed. The five PII2DD2 control gains are updated online with a stable adaptation law driven by Lyapunov’s stability theory. This is the unique advantage of the proposed approach. Furthermore, it is equipped with a novel robust control term to improve controller robustness against system uncertainties and unknown disturbances. An important feature of the proposed approach is that it is a model-free controller. In addition, to determine the control design parameters and avoid trial and error, the Teaching–learning-based optimization algorithm (TLBO) is employed to regulate these parameters and enhance the performance of the proposed controller. Based on the Lyapunov stability theory, it is proven that the proposed control scheme can guarantee the synchronization and the stability of closed-loop control system. The case study is the Duffing–Holmes oscillator. Comparative simulation results are presented which confirm the superiority of the proposed approach. 相似文献
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建设学习型城市已经成为很多地方政府新时期的奋斗目标。地方高校在建设学习型城市中处于基础地位,是建设学习型城市的重要阵地。地方高校以其独特的地位和功能,理应在建设学习型城市中发挥其应有的作用。 相似文献
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In this paper, a modified teaching–learning-based optimisation (mTLBO) algorithm is proposed to solve the re-entrant hybrid flowshop scheduling problem (RHFSP) with the makespan and the total tardiness criteria. Based on the simple job-based representation, a novel decoding method named equivalent due date-based permutation schedule is proposed to transfer an individual to a feasible schedule. At each generation, a number of superior individuals are selected as the teachers by the Pareto-based ranking phase. To enhance the exploitation ability in the promising area, the insertion-based local search is embedded in the search framework as the training phase for the TLBO. Due to the characteristics of the permutation-based discrete optimisation, the linear order crossover operator and the swap operator are adopted to imitate the interactions among the individuals in both the teaching phase and the learning phase. To store the non-dominated solutions explored during the search process, an external archive is used and updated when necessary. The influence of the parameter setting on the mTLBO in solving the RHFSP is investigated, and numerical tests with some benchmarking instances are carried out. The comparative results show that the proposed mTLBO outperforms the existing algorithms significantly. 相似文献
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提出了一种新的基于机器学习的文字检测方法。首先,在网上下载280张图片,其中包括书的封面、CD封面和电影海报,人工标记和提取其中的文字区域。其次,基于对文字区域和非文字区域的统计性差异分析,得到两大类特征集,用于构造弱分类器。然后,使用Ada—boost将上一步得到的弱分类器筛选和组织起来,得到一个二级的级联分类器。最后,通过将图片的子区域分类为文字和非文字区域,此级联分类器就能够检测出文字区域。与其他方法相比,此方法在检测单个字符、倾斜甚至竖直的文字方面有很好的鲁棒性。 相似文献
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A new efficient optimization method, called ‘Teaching–Learning-Based Optimization (TLBO)’, is proposed in this paper for the optimization of mechanical design problems. This method works on the effect of influence of a teacher on learners. Like other nature-inspired algorithms, TLBO is also a population-based method and uses a population of solutions to proceed to the global solution. The population is considered as a group of learners or a class of learners. The process of TLBO is divided into two parts: the first part consists of the ‘Teacher Phase’ and the second part consists of the ‘Learner Phase’. ‘Teacher Phase’ means learning from the teacher and ‘Learner Phase’ means learning by the interaction between learners. The basic philosophy of the TLBO method is explained in detail. To check the effectiveness of the method it is tested on five different constrained benchmark test functions with different characteristics, four different benchmark mechanical design problems and six mechanical design optimization problems which have real world applications. The effectiveness of the TLBO method is compared with the other population-based optimization algorithms based on the best solution, average solution, convergence rate and computational effort. Results show that TLBO is more effective and efficient than the other optimization methods for the mechanical design optimization problems considered. This novel optimization method can be easily extended to other engineering design optimization problems. 相似文献
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In addition to energy consumption, the use of cutting fluids, deposition of worn tools and certain other manufacturing activities can have environmental impacts. All these activities cause carbon emission directly or indirectly; therefore, carbon emission can be used as an environmental criterion for machining systems. In this article, a direct method is proposed to quantify the carbon emissions in turning operations. To determine the coefficients in the quantitative method, real experimental data were obtained and analysed in MATLAB. Moreover, a multi-objective teaching–learning-based optimization algorithm is proposed, and two objectives to minimize carbon emissions and operation time are considered simultaneously. Cutting parameters were optimized by the proposed algorithm. Finally, the analytic hierarchy process was used to determine the optimal solution, which was found to be more environmentally friendly than the cutting parameters determined by the design of experiments method. 相似文献