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Improved Neural Network Control Approach for a Humanoid Arm

JOURNAL OF DYNAMIC SYSTEMS MEASUREMENT AND CONTROL-TRANSACTIONS OF THE ASME 2019年第10期141卷 1-13页

作者： Liu, Xinhua Zhang, Xiaohui Malekian, Reza Sarkodie-Gyan, Th. Li, Zhixiong China Univ Min & Technol Sch Mechatron Engn Xuzhou 211006 Jiangsu Peoples R China Malmo Univ Dept Comp Sci & Media Technol S-20506 Malmo Sweden Univ Texas El Paso Coll Engn 500 West Univ Ave El Paso TX 79968 USA Ocean Univ China Sch Engn Tsingdao 266100 Peoples R China Univ Wollongong Sch Mech Mat Mechatron & Biomed Engn Wollongong NSW 2522 Australia

This study extended the knowledge over the improvement of the control performance for a seven degrees-of-freedom (7DOF) humanoid arm. An improved adaptive Gaussian radius basic function neural network (RBFNN) approach was proposed to ensure the reliability and stability of the humanoid arm control. Considering model uncertainties, the established dynamic model for the humanoid arm was divided into a nominal model and an error model. The error model was approximated by the RBFNN learning to compensate the uncertainties. The contribution of this study mainly concentrates on employing fruit fly optimization algorithm (FOA) to optimize the basic width parameter of the RBFNN, which can enhance the capability of the error approximation speed. Additionally, the output weights of the neural network were adjusted using the Lyapunov stability theory to improve the robustness of the RBFN-based error model. The simulation and experiment results demonstrate that the proposed approach is able to optimize the system state with less tracking errors, regulate the uncertain nonlinear dynamic characteristics, and effectively reduce unexpected interferences.

关键词： adaptive control fruit fly optimization algorithm humanoid arm radial basis function network

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Effective sparse adaboost method with ESN and FOA for industrial electricity consumption forecasting in China

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ENERGY 2018年 155卷 1013-1031页

作者： Wang, Lin Lv, Sheng-Xiang Zeng, Yu-Rong Huazhong Univ Sci & Technol Sch Management Wuhan 430074 Hubei Peoples R China Hubei Univ Econ Wuhan 430205 Hubei Peoples R China

Accurate electricity consumption forecasting is a challenging task for its unstable behavior and influence mechanism based on multiple factors. In this study, a neural network ensemble approach is designed to solve this problem. In the proposed method, a novel sparse adaboost (adaboost(sp)) is designed as the ensemble framework to enhance the generalization ability and reduce ensemble cost, and echo state network (ESN) is adopted to build the nonlinear relationships between electricity demand and multiple factors. An improved fruit fly optimization algorithm (FOA) helps selecting input variables considering their time lag effects. Two industrial electricity consumption (IEC) forecasting applications in China are investigated to verify the effectiveness of proposed ensemble forecasting approach. Numerical results indicate that adaboost(sp)-ESN with FOA can better predict the future IEC than various benchmark methods. Compared with existing boosting ensemble approaches, the proposed adaboostsp is more efficient and can save considerable computation cost. Impacts of selected variables are further examined and results show many industrial indexes have significant time lag effects on IEC. Based on the proposed techniques, future IEC demand in Hubei Province is estimated and analyzed. Application studies demonstrate the proposed hybrid ensemble approach is a practical choice for mid-term IEC adjustment and projection. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Sparse adaboost Echo state network fruit fly optimization algorithm Ensemble forecasting Industrial electricity consumption forecasting

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Free-form surface parts quality inspection optimization with a novel sampling method

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APPLIED SOFT COMPUTING 2018年 62卷 550-570页

作者： Li, Taifeng Gao, Liang Pan, Quanke Li, Peigen Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan Hubei Peoples R China

Quality inspection is a critical step in the process of manufacturing of free-form surface parts. For dimensional quality control, registration is normally used to match the measurement model to the design model. Then, the machined errors can be visualized in the misalignment regions. Because these two models are located in different coordinate systems, an optimal spatial transformation matrix is necessary. In this area, iterative closest point (ICP) is the best known method for registration. However, the global optimum solution is intractable to compute in terms of ICP. Considering of the accuracy and efficiency of free-form surface parts quality inspection, in this paper, the maximin distance approach is introduced for model simplification with normal deviation information. Moreover, to find the optimal transformation parameters, an improved fruit fly optimization algorithm is proposed in terms of the saccade behavior of drosophila. The performance of the developed algorithms has been tested via both computer simulations and real experiments, and the results demonstrate that the proposed methods are superior to several classical approaches for free-form surface parts quality inspection. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Quality inspection Registration Maximin distance method fruit fly optimization algorithm

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Stock Price Prediction based on Grey Relational Analysis and Support Vector Regression 30

Stock Price Prediction based on Grey Relational Analysis and...

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30th Chinese Control and Decision Conference (CCDC)

作者： Hou, Xianxian Zhu, Shaohan Xia, Li Wu, Gang Northeastern Univ Coll Comp Sci & Engn Shenyang 110169 Peoples R China

ISBN: (纸本)9781538612446

Stock market data is extremely large and complicated. In stock prediction research, the selection of technical indicators has not a scientific theory as a guide. This paper proposes a novel method based on Grey Relational Analysis to select the technical indicators. Then make predictions by Support Vector Regression that optimized by improved fruit fly optimization algorithm. Firstly, the fruit fly optimization algorithm is improved by decreasing footstep and simulated annealing. Secondly, the improved fruit fly optimization algorithm is adopted to optimize the penalty factor c and the kernel function parameter g of the support vector regression. Finally, modeling and forecasting of the stock price with optimized support vector regression are conducted and some simulation experiments are carried out. The Support Vector Regression is adept at analyzing small size and multi -dimensional samples, so it is suitable for short-term stock prediction. By comparing with other three methods, the one this paper proposed could fast convergence and improve the accuracy of forecasting and is an efficient and feasible method.

关键词： prediction Grey Relational Analysis Support Vector Regression fruit fly optimization algorithm Simulated Annealing

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fruit fly algorithm Based on Extremal optimization

Fruit Fly Algorithm Based on Extremal Optimization

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International Conference on Computational Intelligence and Security

作者： Shuiping Zhang Yang Chen Yangdan Geng School of Information Engineering Jiangxi University of Science and Technology Ganzhou Jiangxi China

ISBN: (纸本)9781509048410

Aiming at the problem that solutions obtained by simply using FOA (fruit fly optimization algorithm) would fall into local optimum, with slow convergence and low accuracy, we therefore propose an algorithm EOFOA that combines EO (Extremal optimization algorithm) with FOA. EOFOA brings the basic idea of EO, an algorithm of extremal dynamics, into FOA, so as to prevent local optimum and increase convergence accuracy by taking advantages of changing the initial distribution strategy, improving the diversity of the fruit fly population through randomly replacing the individuals that are not adaptive to the population, and making use of its strong volatility that enables the algorithm to search continuously. Simulation results show that: EOFOA is more accurate than FOA and the convergence is also quickened, which is efficient in avoiding premature convergence.

关键词： Local optimum fruit fly optimization algorithm Extremal optimization algorithm Premature convergence

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Multi-objective train operation optimization based on fusion distance and preference information

Multi-objective train operation optimization based on fusion...

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Chinese Automation Congress (CAC)

作者： Liu Kaiwei Wang Xingcheng Wang Longda Liu Gang Dalian Maritime Univ Ship Elect Engn Coll Dalian Peoples R China Inner Mongolia Univ Nationalities Coll Mech Engn Tongliao Peoples R China

ISBN: (纸本)9781728113128

In this paper. the complex multi-objective optimization problem is investigated for the train operation process. To sovle the multi-objective optimization problem, the fruit fly optimization algorithm (FOA) is adopted. Fistly, a new method of distance fusion, which fuses the Mahalanobis distance and the Euclidean distance, is proposed to solve the problem that the classical Mahalanobis distance and Euclidean distance can't. effectively calculate the actual distance between the individual and the extremum individual solution set caused by the correlation ambiguity of characteristic variables. Then, to maintain the population diversity, the preference information is adopted so that the optimal solution provided by FOA can significantly move to the desired region. Hence, both problems can be overcome by the improved FOA based on fusion distance and preference information. Finally, simulation results are carried out to show the efficacy of the proposed method.

关键词： train operation optimization multi-objective optimization fruit fly optimization algorithm distance measurement preference information

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A new optimization particle filtering navigation location method for aquatic plants cleaning workboat in crab farming

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INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS 2018年第4期15卷

作者： Ruan, Chengzhi Zhao, Dean Ding, Shihong Sun, Yueping Rao, Jinhui Liu, Xiaoyang Jia, Weikuan Wuyi Univ Sch Mech & Elect Engn 358 Baihua Rd Wuyishan 354300 Peoples R China Jiangsu Univ Sch Elect & Informat Engn Zhenjiang Peoples R China Shandong Normal Univ Sch Informat Sci & Engn Jinan Shandong Peoples R China

Chinese river crabs are important aquatic products in China, and the accurate operation of aquatic plants cleaning workboat is an urgent need for solving various problems in the aquaculture process. In order to achieve the accurate navigation positioning, this article introduces the visual-aided navigation system and combines the advantages of particle filter in nonlinear and non-Gaussian systems. Meanwhile, the generalized regression neural network is used to adjust the particle weights so that the samples are closer to the posterior density, thus avoiding the phenomenon of particle degradation and keeping the diversity of particles. In order to improve the network performance, the fruit fly optimization algorithm is introduced to adjust the smoothing factor of transfer function for the generalized regression neural network model layer. On this basis, the location filtering navigation method based on fruit fly optimization algorithm-generalized regression neural network-particle filter is proposed. According to the simulation results, the mean(R) of root-mean-square error of the proposed fruit fly optimization algorithm-generalized regression neural network-particle filter method decreases by 12.39% and 6.87%, respectively, compared with those of particle filter and generalized regression neural network methods, and the mean(T) of running time decreases by 16.04% and 9.14%, respectively. From the repeated experiments on the aquatic plants cleaning workboat in crab ponds, the latitude error of the proposed method decreases by 23.45% and 12.68%, respectively, and that the longitude error decreases by 29.11% and 17.65%, respectively, compared with those of particle filter and generalized regression neural network methods. It is proved that our proposed method can effectively improve the navigation positioning accuracy of aquatic plants cleaning workboat.

关键词： Crab farming aquatic plants cleaning workboat visual navigation particle filter generalized regression neural network fruit fly optimization algorithm

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A novel fruit fly framework for multi-objective shape design of tubular linear synchronous motor

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JOURNAL OF SUPERCOMPUTING 2017年第3期73卷 1235-1256页

作者： Rizk-Allah, Rizk M. El-Sehiemy, Ragab A. Deb, Suash Wang, Gai-Ge Univ Minoufia Dept Basic Engn Sci Shibin Al Kawm Egypt Kafrelsheikh Univ Dept Elect Engn Fac Engn Kafr Al Sheikh Egypt Jiangsu Normal Univ Sch Comp Sci & Technol Xuzhou 221116 Jiangsu Peoples R China Univ Alberta Dept Elect & Comp Engn Edmonton AB T6R 2V4 Canada Northeast Normal Univ Sch Comp Sci & Informat Technol Changchun 130117 Peoples R China Northeast Normal Univ Inst Algorithm & Big Data Anal Changchun 130117 Peoples R China

This paper addresses a novel multi-objective fruit fly optimization algorithm (MOFOA) for solving multi-objective optimization problems. The essence of MOFOA lies in its having two characteristic features. For the first feature, a population of random fruit flies initializes the algorithm. During this initialization phase, the dominated fruit fly is replaced by the nearest non-dominated one. Subsequently, the fruit flies undergo evolution by flying randomly around the non-dominated solution or around the reference point, i.e., the best location of the individual objectives. Afterwards, the fruit flies are updated according to the nearest location whether from the reference point or the previous non-dominated location. For the second feature, the weighted sum method is incorporated to update the previous best locations of fruit flies and the reference point to emphasize the convergence of the non-dominated solutions. To prove the capability of the proposed MOFOA, two standard benchmark problems in addition to the real world application, namely, multi-objective shape design of tubular linear synchronous motor (TLSM) are checked. The corresponding TLSM objective functions aims to maximize operating force and to minimize the flux saturation. The outcomes clearly demonstrate the effectiveness of the proposed algorithm for finding the non-dominated solutions.

关键词： fruit fly optimization algorithm Multi-objective optimization problem Pareto-optimal solutions Reference point Tubular linear synchronous motor

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A hybrid fruit fly algorithm for solving flexible job-shop scheduling to reduce manufacturing carbon footprint

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JOURNAL OF CLEANER PRODUCTION 2017年 168卷 668-678页

作者： Liu, Qiong Zhan, Mengmeng Chekem, Freddy O. Shao, Xinyu Ying, Baosheng Sutherland, John W. Huazhong Univ Sci & Technol Sch Mech Sci & Engn State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Hubei Peoples R China Wuhan Univ Sci & Technol Sch Automobile & Traff Engn Wuhan 430081 Hubei Peoples R China Purdue Univ Environm & Ecol Engn W Lafayette IN 47906 USA

In order to help manufacturing companies to quantify product carbon footprints and provide carbon emission data in a job shop for future life cycle product carbon labelling, a calculating method of product carbon footprint in a job shop is proposed. The carbon emissions in a job shop are allocated to each product based on mapping relations between resources and products consuming the resources. To reduce product carbon footprints in a job shop, a multi-objective optimization model aimed at minimizing carbon footprints of all products and makespan is proposed. In order to solve the proposed model and explore the performance of fruit fly optimization algorithm (FOA) in multi-objective scheduling optimization problems, a Hybrid fruit fly optimization algorithm (HFOA) is designed. The proposed model and algorithm are verified by a case study. It is to provide decision makers awareness of carbon footprints for each product in a job shop and the product carbon footprint in job shops can be further used to calculate life cycle product carbon labelling. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Green manufacturing Flexible job-shop scheduling Carbon emission Carbon footprint fruit fly optimization algorithm

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Performance Prediction of Switched Reluctance Motor using Improved Generalized Regression Neural Networks for Design optimization

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CES Transactions on Electrical Machines and Systems 2018年第4期2卷 371-376页

作者： Zhu Zhang Shenghua Rao Xiaoping Zhang School of Information and Electrical Engineering Hunan University of Science and TechnologyHunanChina Resources Exploration Equipment and Safety Technology Hunan University of Science and TechnologyHunanChina

Since practical mathematical model for the design optimization of switched reluctance motor(SRM)is difficult to derive because of the strong nonlinearity,precise prediction of electromagnetic characteristics is of great importance during the optimization *** this paper,an improved generalized regression neural network(GRNN)optimized by fruit fly optimization algorithm(FOA)is proposed for the modeling of SRM that represent the relationship of torque ripple and efficiency with the optimization variables,stator pole arc,rotor pole arc and rotor yoke *** element parametric analysis technology is used to obtain the sample data for GRNN training and *** comparisons and analysis among back propagation neural network(BPNN),radial basis function neural network(RBFNN),extreme learning machine(ELM)and GRNN is made to test the effectiveness and superiority of FOA-GRNN.

关键词： fruit fly optimization algorithm generalized regression neural networks switched reluctance motor

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