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Nonlinear system identification using a cuckoo search optimized adaptive Hammerstein model

EXPERT SYSTEMS WITH APPLICATIONS 2015年第5期42卷 2538-2546页

作者： Gotmare, Akhilesh Patidar, Rohan George, Nithin V. Indian Inst Technol Dept Elect Engn Gandhinagar 382424 Gujarat India

An attempt has been made in this paper to model a nonlinear system using a Hammerstein model. The Hammerstein model considered in this paper is a functional link artificial neural network (FLANN) in cascade with an adaptive infinite impulse response (IIR) filter. In order to avoid local optima issues caused by conventional gradient descent training strategies, the model has been trained using a cuckoo search algorithm (CSA), which is a recently proposed stochastic algorithm. Modeling accuracy of the proposed scheme has been compared with that obtained using other popular evolutionary computing algorithms for the Hammerstein model. Enhanced modeling capability of the CSA based scheme is evident from the simulation results. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Hammerstein model System identification particle swarm optimization algorithm Differential evolution Cuckoo search algorithm

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Application of a two-stage fuzzy neural network to a prostate cancer prognosis system

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ARTIFICIAL INTELLIGENCE IN MEDICINE 2015年第2期63卷 119-133页

作者： Kuo, Ren-Jieh Huang, Man-Hsin Cheng, Wei-Che Lin, Chih-Chieh Wu, Yung-Hung Natl Taiwan Univ Sci & Technol Dept Ind Management Taipei 106 Taiwan Hon Hai Precis Ind Co Ltd New Taipei City 236 Taiwan Taipei Vet Gen Hosp Inst Clin Med Dept Urol Taipei 112 Taiwan Natl Yang Ming Univ Sch Med Dept Urol Taipei 112 Taiwan Taipei Vet Gen Hosp Superintendent Off Taipei 112 Taiwan

Objective: This study intends to develop a two-stage fuzzy neural network (FNN) for prognoses of prostate cancer. Methods: Due to the difficulty of making prognoses of prostate cancer, this study proposes a two-stage FNN for prediction. The initial membership function parameters of FNN are determined by cluster analysis. Then, an integration of the optimization version of an artificial immune network (Opt-aiNET) and a particle swarm optimization (PSO) algorithm is developed to investigate the relationship between the inputs and outputs. Results: The evaluation results for three benchmark functions show that the proposed two-stage FNN has better performance than the other algorithms. In addition, model evaluation results indicate that the proposed algorithm really can predict prognoses of prostate cancer more accurately. Conclusions: The proposed two-stage FNN is able to learn the relationship between the clinical features and the prognosis of prostate cancer. Once the clinical data are known, the prognosis of prostate cancer patient can be predicted. Furthermore, unlike artificial neural networks, it is much easier to interpret the training results of the proposed network since they are in the form of fuzzy IF THEN rules. These rules are very important for medical doctors. This can dramatically assist medical doctors to make decisions. (C) 2014 Elsevier B.V. All rights reserved.

关键词： optimization version of an artificial immune network particle swarm optimization algorithm Fuzzy neural network Prostate cancer Prognosis

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An Improved particle swarm optimization Based on Adaptive Mutation and P Systems for Micro-grid Economic Operation

An Improved Particle Swarm Optimization Based on Adaptive Mu...

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2015年中国智能自动化学术会议

作者： Zhang Sun Tao Liu Jun Wang Juan Luo Hong Li School of Electrical and Information Engineering Xihua University

A particle swarm optimization(PSO) algorithm based on adaptive mutation and P systems is proposed to overcome trapping in local optimum solution and low optimization precision in this paper. The algorithm combines the evolutionary rules of PSO, the strategy of adaptive mutation with the hierarchical membrane structure, and communication rules of P systems. At the same time, in order to achieve rapid economic operation and effectiveness of the micro-grid the proposed algorithm is investigated in experiments which are based on the function optimization of micro-grid's economic operation. Furthermore, the feasibility and effectiveness of the proposed algorithm are shown in the experimental results.

关键词： particle swarm optimization algorithm P systems Function optimization Economic operation

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Determination of Rod's Position by the Shadow

Determination of Rod's Position by the Shadow

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2016 4th International Conference on Advanced Materials and Information Technology Processing(AMITP 2016)

作者： Saishuo Zheng School of North China Electric Power University

To find out the position of the rod with its shadow, a model for determining the position of the straight rod with the objective function of minimizing the error value is established. Some results had been obtained by particle swarm optimization algorithm, and were classified by the system cluster analysis to achieve the optimal results. After that, the optimal results were tested.

关键词： Shadow positioning particle swarm optimization algorithm Exhaustive method

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BIM-based integrated approach for detailed construction scheduling under resource constraints

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AUTOMATION IN CONSTRUCTION 2015年 53卷 29-43页

作者： Liu, Hexu Al-Hussein, Mohamed Lu, Ming Univ Alberta Dept Civil & Environm Engn Edmonton AB T6G 2W2 Canada

Building information modeling (BIM) has been recognized as an information technology with the potential to markedly change the Architecture, Engineering, and Construction (AEC) industry, and has drawn attention from numerous scholars within the construction domain. Despite the reported advancements pertaining to BIM in previous studies, the extended use of BIM has not yet reached its full potential. This paper thus presents a BIM-based integrated scheduling approach which facilitates the automatic generation of optimized activity-level construction schedules for building projects under resource constraints, by achieving an in-depth integration of BIM product models with work package information, process simulations, and optimization algorithms. A developed prototype system for panelized building construction as an add-on tool for Autodesk Revit is described. A case study is subsequently presented to demonstrate the methodology. Building on the existing body of research in this field, the key contribution of the proposed research is the in-depth integration of BIM product model with work package information, process simulations, and an optimization model, which provides solutions addressing the challenges of the existing practice with respect to detailed construction scheduling under resource constraints. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Building information modeling Integrated approach Work package information Process simulation model particle swarm optimization algorithm Detailed scheduling

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Assimilation of Two Variables Derived from Hyperspectral Data into the DSSAT-CERES Model for Grain Yield and Quality Estimation

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REMOTE SENSING 2015年第9期7卷 12400-12418页

作者： Li, Zhenhai Wang, Jihua Xu, Xingang Zhao, Chunjiang Jin, Xiuliang Yang, Guijun Feng, Haikuan Beijing Acad Agr & Forestry Sci Beijing Res Ctr Informat Technol Agr Beijing 100097 Peoples R China Natl Engn Res Ctr Informat Technol Agr Beijing 100097 Peoples R China Zhejiang Univ Inst Agr Remote Sensing & Informat Applicat Hangzhou 310029 Zhejiang Peoples R China Beijing Acad Agr & Forestry Sci Beijing Res Ctr Agr Stand & Testing Beijing 100097 Peoples R China Minist Agr Key Lab Informat Technol Agr Beijing 10097 Peoples R China Beijing Engn Res Ctr Agr Internet Things Beijing 100097 Peoples R China Chinese Acad Sci Northeast Inst Geog & Agroecol Key Lab Wetland Ecol & Environm Changchun 130102 Peoples R China

The combination of remote sensing and crop growth models has become an effective tool for yield estimation and a potential method for grain quality estimation. In this study, two assimilation variables (derived from a hyperspectral sensor), called leaf area index (LAI) and canopy nitrogen accumulation (CNA), were jointly used to calibrate the sensitive parameters and initial states of the DSSAT-CERES crop model, to improve simulated output of the grain yield and protein content of winter wheat. The results show that the modified simple ratio (MSR) and normalized difference red edge (NDRE) better estimated LAI and CNA, respectively, compared with the other possible vegetation indices. The integration of both LAI and CNA resulted in a more robust DSSAT-CERES models with than each one alone. The R-2 and RMSE values, respectively, of the regression between the simulated (using the two assimilation variables method) and measured LAI were 0.828 and 0.494, and for CNA were 0.808 and 20.26 kg ***(-1). These two assimilation variables resulted in grain yield and protein content estimates of winter wheat with a high precision and R-2 and RMSE values of 0.698 and 0.726 ***(-1), and 0.758% and 1.16%, respectively. This study provides a more robust method for estimating the grain yield and protein content of winter wheat based on the integration of the DSSAT-CERES crop model and remote sensing data.

关键词： Hyperspectral DSSAT-CERES winter wheat particle swarm optimization algorithm yield grain protein content

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GA-PSO Integration algorithm and Its Application in Modeling on Furnace Pressure System

GA-PSO Integration Algorithm and Its Application in Modeling...

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2016 2nd Workshop on Advanced Research and Technology in Industry Applications(WARTIA 2016)

作者： Qiming Chen Department of Automation North China Electric Power University

PSO algorithm is a kind of swarm intelligence optimization algorithm which has the advantages of simple principle,easy implementation,few parameters needed to adjust and so ***,the search accuracy of the basic PSO algorithm still needs to be *** this paper,a modified PSO algorithm using exponent decline inertia weight is put forward and successfully applied to the parameter identification of the furnace pressure *** modified PSO algorithm combines the nonlinear optimization and genetic algorithm to optimize the inertia weight and acceleration constants of the basic PSO algorithm,and is proved to be effective in parameter identification.

关键词： particle swarm optimization algorithm genetic algorithm system identification furnace pressure system

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Bilevel and multi-objective dynamic construction site layout and security planning

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AUTOMATION IN CONSTRUCTION 2015年 57卷 1-16页

作者： Li, Zongmin Shen, Wenjing Xu, Jiuping Lev, Benjamin Sichuan Univ Sch Business Chengdu 610064 Peoples R China Sichuan Univ State Key Lab Hydraul & Mt River Engn Chengdu 610064 Peoples R China Drexel Univ Dept Decis Sci LeBow Coll Business Philadelphia PA 19104 USA

The joint problem of dynamic construction site layout and security planning is fundamental to any construction project. However, it is still largely unexplored. Few research studies investigated the implementation of security measures during the construction phase. This paper proposes a bilevel multi-objective model for the dynamic construction site layout and security planning problem. Specifically, the upper-level programming denotes that the project manager must first choose the construction site layout and security strategies to minimize the layout costs and consequences of a potential attack. The lower-level programming denotes that the attacker will destroy a subset of the facilities to inflict the maximum economic consequence on the construction facilities system. Thereafter, a bilevel multi-objective particle swarm optimization algorithm (MOBLPSO) is designed to solve this model. Finally, the approach is carried out in the Xiajiang hydropower large-scale construction project to illustrate the effectiveness of the proposed model and algorithm. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Dynamic construction site layout and security Bilevel multi-objective model particle swarm optimization algorithm

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Multiobjective optimization of process parameters for plastic injection molding via soft computing and grey correlation analysis

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2015年第1-4期78卷 525-536页

作者： Xu, Gang Yang, Zhitao Nanchang Univ Dept Math Nanchang 330031 Peoples R China S China Univ Technol Natl Engn Res Ctr Novel Equipment Polymer Proc Guangzhou 510641 Guangdong Peoples R China

In the plastic injection molding (PIM), the optimization of the process parameters is a complex task. The objective of this study is to propose an intelligent approach for efficiently optimizing PIM parameters when multiple objectives are involved, where different objectives, such as minimizing part weight, flash, or volumetric shrinkage, present trade-off behaviors. Multiple objective functions reflecting the product quality are constructed for the optimization model of PIM parameters. The proposed approach integrates Taguchi's parameter design method, back-propagation neural network (BPNN), grey correlation analysis (GCA), particle swarm optimization (PSO) and multiobjective particle swarm optimization (MOPSO) to locate the Pareto optimal solution for multiobjective optimization problem. PSO and GCA are applied to optimize the network structure of BPNN to establish multiobjective mathematical model (PSO-GCANN) that finely maps the relationship between the input process parameters and output multiresponse. MOPSO is used to fine-tune the Pareto optimal solutions while the approximate PSO-GCANN is utilized to efficiently compute the fitness of every individual during the evolution of MOPSO. The illustrative application and comparison of results show that the proposed methodology outperforms the existing methods and can help mold designers to efficiently and effectively identify optimal process parameters.

关键词： Plastic injection molding Back-propagation neural networks Grey correlation analysis particle swarm optimization algorithm Multiobjective optimization

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A hybrid multi-objective PSO-EDA algorithm for reservoir flood control operation

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APPLIED SOFT COMPUTING 2015年 34卷 526-538页

作者： Luo, Jungang Qi, Yutao Xie, Jiancang Zhang, Xiao Xian Univ Technol State Key Lab Base Ecohydraul Engn Arid Area Xian 710048 Shaanxi Peoples R China Xian Univ Technol Inst Water Resources & Hydroelect Engn Xian 710048 Shaanxi Peoples R China Xidian Univ Sch Comp Sci & Technol Xian Peoples R China

Reservoir flood control operation (RECO) is a complex multi-objective optimization problem (MOP) with interdependent decision variables. Traditionally, RECO is modeled as a single optimization problem by using a certain scalar method. Few works have been done for solving multi-objective RECO (MO-RECO) problems. In this paper, a hybrid multi-objective optimization approach named MO-PSO-EDA which combines the particle swarm optimization (PSO) algorithm and the estimation of distribution algorithm (EDA) is developed for solving the MO-RECO problem. MO-PSO-EDA divides the particle population into several sub-populations and builds probability models for each of them. Based on the probability model, each sub-population reproduces new offspring by using PSO based and EDA methods. In the PSO based method, a novel global best position selection method is designed. With the help of the EDA based reproduction, the algorithm can lean linkage between decision variables and hence have a good capability of solving complex multi-objective optimization problems, such as the MO-RECO problem. Experimental studies on six benchmark problems and two typical multi-objective flood control operation problems of Ankang reservoir have indicated that the proposed MO-PSO-EDA performs as well as or superior to the other three competitive multi-objective optimization algorithms. MO-PSO-EDA is suitable for solving MO-RECO problems. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Multi-objective optimization Hybrid algorithm particle swarm optimization algorithm Estimation of distribution algorithm Reservoir flood control operation

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