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Optimization of architectural art teaching model based on Naive Bayesian classification algorithm and fuzzy model

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020年第2期39卷 1965-1976页

作者： Liu, Ying Inner Mongolia Univ Technol Sch Architecture Hohhot Inner Mongolia Peoples R China Inner Mongolia Engn Technol & Res Ctr Green Bldg Hohhot Inner Mongolia Peoples R China

At present, the teaching of architectural art in China is still relatively traditional, and there are still some problems in the actual teaching. Based on this, this study combines the Naive Bayesian classification algorithm with the fuzzy model to construct a new architectural art teaching model. In teaching, the Naive Bayesian classification algorithm generates only a small number of features for each item in the training set, and it only uses the probability calculated in the mathematical operation to train and classify the item. Moreover, by combining the fuzzy model, the materials needed for architectural art teaching can be quickly generated, and the teaching principles and implementation strategies of architectural art are summarized. In addition, this paper proposes an attribute weighted classification algorithm combining differential evolution algorithm with Naive Bayes. The algorithm assigns weights to each attribute based on the Naive Bayesian classification algorithm and uses differential evolution algorithm to optimize the weights. The research shows that the method proposed in this paper has certain effect on the optimization of architectural art teaching mode.

关键词： Bayesian classification algorithm fuzzy model architectural art differential evolution algorithm

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The calculation of the reliability index in nonlinear problems: A new method

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AIN SHAMS ENGINEERING JOURNAL 2020年第3期11卷 705-716页

作者： Alamdarloo, Pezhman Derakhshan Moghaddam, Mehdi Azhdary Univ Sistan & Baluchestan Civil Engn Dept Zahedan Iran

The accurate estimation of the health (reliability) index is important to estimate probability of failure in the reliability assessment of structures. The conventional first-order reliability methods including the Hasofer-Lind, Rackwitz-Fiessler, and Monte Carlo could lead to unstable, fluctuating, and distorted solutions to nonlinear problems, featuring complicated structural performance functions. The present study aimed to propose a new method by combining the particle swarm optimization and differential evolution algorithms in order to calculate the reliability index. The performance of the proposed method was evaluated by 10 examples from different studies, and the convergence results were compared to the results of studies such as Hasofer-Lind, Rackwitz-Fiessler, Monte Carlo and some other methods. To verify the accuracy of the proposed method, to verify the accuracy of the proposed method, a reliability index chart was applied. The comparisons indicated the high accuracy and speed of the proposed method. Accordingly, in higher order nonlinear problems, the proposed method successfully calculated the reliability index while some failed to solve these problems. (C) 2019 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Ain Shams University.

关键词： Reliability index Combination of algorithms Particle swarm optimization algorithm differential evolution algorithm

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Absolute Positioning Accuracy Improvement in an Industrial Robot

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SENSORS 2020年第16期20卷 4354-4354页

作者： Jiang, Yizhou Yu, Liandong Jia, Huakun Zhao, Huining Xia, Haojie Hefei Univ Technol Sch Instrument Sci & Optoelect Engn Hefei 230009 Peoples R China

The absolute positioning accuracy of a robot is an important specification that determines its performance, but it is affected by several error sources. Typical calibration methods only consider kinematic errors and neglect complex non-kinematic errors, thus limiting the absolute positioning accuracy. To further improve the absolute positioning accuracy, we propose an artificial neural network optimized by the differential evolution algorithm. Specifically, the structure and parameters of the network are iteratively updated by differential evolution to improve both accuracy and efficiency. Then, the absolute positioning deviation caused by kinematic and non-kinematic errors is compensated using the trained network. To verify the performance of the proposed network, the simulations and experiments are conducted using a six-degree-of-freedom robot and a laser tracker. The robot average positioning accuracy improved from 0.8497 mm before calibration to 0.0490 mm. The results demonstrate the substantial improvement in the absolute positioning accuracy achieved by the proposed network on an industrial robot.

关键词： absolute positioning accuracy industrial robot neural network differential evolution algorithm

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Aerodynamic Parameter Identification for High-Spin Projectile Based on differential evolution-Maximum Likelihood Method 38

Aerodynamic Parameter Identification for High-Spin Projectil...

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38th Chinese Control Conference (CCC)

作者： Hou, Xian-qin Wang, Liang-ming Fu, Jian Chen, Zhi Nanjing Univ Sci & Technol Sch Energy & Power Engn Nanjing 210094 Peoples R China Shanghai Univ Sch Mechatron Engn & Automat Shanghai 200072 Peoples R China

ISBN: (纸本)9789881563972

In this paper, a differential evolution-Maximum Likelihood algorithm (DEML) is proposed for the drag coefficient identification in high-spin projectile with insensitively initial value selection. The differential evolution method is utilized to replace newton iterative gradient optimization in maximum likelihood estimation theory to improve the accuracy of coefficient identification. And experiment atmosphere data is used to produce the simulation flight data with external disturbances. Based on the flight data, identification simulation results show the effective performance and low-sensitive initial value selection with the DEML method.

关键词： high-spin projectile aerodynamic parameter identification maximum likelihood estimation differential evolution algorithm

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differential evolution Method for Optimal Size and Localization of Photovoltaic in the Algerian Transmission Power System

Differential Evolution Method for Optimal Size and Localizat...

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Algerian Large Electrical Network Conference (CAGRE)

作者： Mosbah, Mustafa Mohammedi, Ridha-Djamel Arif, Salem RDC Etud Chez Soc Algerienne Distribut Elect & Gaz Algiers Algeria Djelfa Univ Elect Engn Dept Djelfa Algeria Univ Amar Telidji Elect Engn Dept LACoSERE Laghouat Algeria

ISBN: (纸本)9781728118239

The integration of photovoltaic source (PVS), brings new challenges to power system operation. This paper proposed the determination of the optimal site and size of PVS in electrical network using differential evolution (DE) method considering technical and security constraints to minimize active loss. The DE technique is applied on Algerian 114 bus power system using MATLAB software.

关键词： photovoltaic size and localization loss reduction power system optimal power flow differential evolution algorithm

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A two-phase approach for the design of two-degree-of-freedom of robust controller for higher order interval system

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INTERNATIONAL JOURNAL OF AUTOMATION AND CONTROL 2020年第2期14卷 161-186页

作者： Rao, D. Srinivasa Kumar, Mangipudi Siva Raju, M. Ramalinga Gudlavalleru Engn Coll Dept Elect & Elect Engn Gudlavalleru AP India JNT Univ Kakinada Dept Elect & Elect Engn Univ Coll Engn Kakinada AP India

This paper proposes a novel method for the design of the robust controller to retain both the robust stability and performance of the higher order interval system via reduced order model using the differential evolution (DE) algorithm. A stable reduced interval model is generated from a higher order interval system using DE in order to minimise the cost and reduce the complexity of the system. The reduced order interval numerator and denominator polynomials are determined by minimising the integral squared error (ISE) using the DE. From the reduced order interval model, a robust PI controller is designed based on the new stability conditions of interval system. The designed robust controller from the reduced order interval model will be attributed to the higher order interval system. The designed PI controller from the proposed method not only stabilises the reduced order model, but also stabilises the original higher order system. Finally, with the help of frequency domain method a pre-filter is constructed to improve the performance of interval system. The viability of the proposed methodology is illustrated through a numerical example for its successful implementation.

关键词： interval system Kharitonov's theorem robust controller model order reduction differential evolution algorithm

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Order Sequencing Problem in a Robotic Mobile Fulfillment System

Order Sequencing Problem in a Robotic Mobile Fulfillment Sys...

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第34届中国控制与决策会议

作者： Hanqi Li Yaoxin Zhang Zhiguo Xiao Dongni Li School of Computer Science Beijing Institute of Technology College of Computer Science and Technology Changchun University Southeast Academy of Information Technology Beijing Institute of Technology

With rapid development of Business-to-Customer(B2 C) e-commerce,enormous goods assortment and fluctuated demand are presented in customer *** manual warehouses have the demerits of low picking efficiency and high human cost,which misfit B2 C *** resolve the difficulties,a Robotic Mobile Fulfillment System(RMFS) is introduced and *** paper studies the order sequencing problem in an RMFS with the situation that a rack can be reused among multiple picking stations in one rack *** order to solve the problem,a Q-learning-based differential evolution algorithm is *** with the existent algorithms,numerical experiments show that the proposed algorithm makes an evident improvement on order picking efficiency.

关键词： Robotic Mobile Fulfillment System order sequencing differential evolution algorithm Q-learning

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Non-Intrusive Appliance Load Monitoring in an Intelligent Device at the Edge layer

Non-Intrusive Appliance Load Monitoring in an Intelligent De...

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International Joint Conference on Neural Networks (IJCNN) held as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Alonso Aguirre-Nunez, Jose Pablo Serrano-Rubio, Juan Herrera-Guzman, Rafael Tecnol Nacl Mexico ITS Irapuato Informat Technol Lab Guanajuato Mexico Ctr Res Math CIMAT Math Dept Guanajuato Mexico

ISBN: (纸本)9781728169262

Nowadays, companies and researchers are developing devices that are connected over the Internet to create new services for users through the collection and analysis of data obtained from sensors. The information obtained from the sensors is collected in the cloud. However, there is a new approach called edge computing whose goal is to process information at the edge of a network instead of processing it in the cloud. Edge computing, combined with machine learning algorithms, has become a powerful tool to optimise tasks in both industrial processes and everyday life. This combination allows decision making in real-time since the data processing is carried out in the place where it is being acquired. In this paper we propose a Non-Intrusive Appliance Load Monitoring (NIALM) which has two functions: a) send detailed energy consumption information to the data server only when it is necessary, and to process the information using an intelligent algorithm based on an Artificial Neural Network to recognise when and how much energy the appliances are consuming. The PCB design of the board includes the ESP12-S microchip. We evaluated the evolutionary Hyperplane Neural Network against the evolutionary Spherical Neural Network to decide the best algorithm for our proposed method. The evolutionary Artificial Neural Networks are trained using the differential evolution algorithm. According to the numerical experiments, the evolutionary Hyperplane Neural Network showed a better performance of classification up to 82%.

关键词： IoT Edge computing differential evolution algorithm Artificial Neural Network

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Chaotic differential evolution algorithm Based on Simplex Method for Large-scale Industrial Processes of Fuzzy Model

Chaotic Differential Evolution Algorithm Based on Simplex Me...

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24th Chinese Control and Decision Conference (CCDC)

作者： He, Dakuo Wang, Lifeng Li, Shuo Yang, Bingyu Northeastern Univ Sch Informat Sci & Engn Shenyang Peoples R China

ISBN: (纸本)9781457720727

In the large-scale industrial processes, there are more slow perturbations. So the mathematical model of an actual system is difficult to be accurate. When optimizing large-scale industrial processes, the mathematical model and the actual system does not match, that is model-reality difference. In order to deal with this problem, the structure of decomposition and coordination is used in this paper. The whole large-scale industrial processes can be decomposed into several sub processes that are interactive, and the simplex method including open-loop simplex method and the simplex method with global feedback is the coordinate strategy. The main idea of the differential evolution algorithm with simple method is that find out the best individual in every generation with the standard differential evolution algorithm method, then around the best individual do local search for some times and compared the former best one with the ones from the local search, if the latter is better, substitute the best one with the latter. A classical example of large-scale industrial processes is applied and the simulation results show the validity of the method.

关键词： fuzzy nonlinear programming fuzzy model large-scale industrial processes simplex method differential evolution algorithm

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Metaheuristic task scheduling algorithms for cloud computing environments

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2022年第9期34卷

作者： Aktan, Merve Nur Bulut, Hasan Aydin Adnan Menderes Dept Comp Engn Aydin Turkey Ege Univ Dept Comp Engn TR-35100 Izmir Turkey

Cloud computing has the advantage of providing flexibility, high-performance, pay-as-you-use, and on-demand service. One of the important research issues in cloud computing is task scheduling. The purpose of scheduling is to assign tasks to available resources while providing optimization on some objectives. Tasks have diversified characteristics, and resources are heterogeneous. These properties make task scheduling an NP-complete problem. In this study, metaheuristic and hybrid metaheuristic algorithms are developed for task scheduling problems in cloud computing environments. We have developed genetic algorithm (GA), differential evolution (DE), and simulated annealing (SA) based metaheuristic algorithms, which are also combined with a greedy approach (GR). In addition to this, we have developed hybrid metaheuristics algorithms, called DE-SA and GA-SA, which are also combined with a greedy approach. The proposed approaches are evaluated in terms of completion time and load balancing of virtual machines. In terms of average completion time, as the number of tasks increases, it has been observed that the DESA algorithm outperforms the solely used DE and SA algorithms. In addition, experiments show that hybrid algorithms improve both the average completion time and the average standard deviation of virtual machine loads for some task groups.

关键词： cloud computing differential evolution algorithm genetic algorithm metaheuristic algorithms simulated annealing task scheduling

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