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Prediction of Dissolved Gas Concentrations in Transformer Oil Based on the KPCA-FFOA-GRNN Model

ENERGIES 2018年第1期11卷 225页

作者： Lin, Jun Sheng, Gehao Yan, Yingjie Dai, Jiejie Jiang, Xiuchen Shanghai Jiao Tong Univ Dept Elect Engn Shanghai 200240 Peoples R China

The purpose of analyzing the dissolved gas in transformer oil is to determine the transformer's operating status and is an important basis for fault diagnosis. Accurate prediction of the concentration of dissolved gas in oil can provide an important reference for the evaluation of the state of the transformer. A combined predicting model is proposed based on kernel principal component analysis (KPCA) and a generalized regression neural network (GRNN) using an improved fruit fly optimization algorithm (FFOA) to select the smooth factor. Firstly, based on the idea of using the dissolved gas ratio of oil to diagnose the transformer fault, gas concentration ratios are also used as characteristic parameters. Secondly, the main parameters are selected from the feature parameters using the KPCA method, and the GRNN is then used to predict the gas concentration in the transformer oil. In the training process of the network, the FFOA is used to select the smooth factor of the neural network. Through a concrete example, it is shown that the method proposed in this paper has better data fitting ability and more accurate prediction ability compared with the support vector machine (SVM) and gray model (GM) methods.

关键词： dissolved gas in oil kernel principal component analysis fruit fly optimization algorithm generalized regression neural network

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Two-step based feature selection method for filtering redundant information

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2017年第4期33卷 2059-2073页

作者： Wang, Youwei Feng, Lizhou Li, Yang Cent Univ Finance & Econ Sch Informat Beijing 100081 Peoples R China Tianjin Univ Finance & Econ Sch Sci & Engn Tianjin Peoples R China

In text classification field, many classifiers cannot deal with the features with large dimensions, thus it is very important to filter the redundant information from the original feature space efficiently and achieve the features with best qualities. On this basis, a new two-step based feature selection method is proposed in this paper. Firstly, some definitions (word semantic correlation, set semantic correlation, semantic correlative and semantic correlative set) are introduced, and the algorithm of generating the semantic correlative sets is given. Secondly, the process of the two-step based feature selection method is described: in the first step, a feature subset is obtained by using an optimal feature selection method, and a set of semantic correlative sets is generated by using the selected feature subset;in the second step, the redundant information of the selected features is filtered by using the generated semantic correlative sets. Finally, in order to avoid local optimum when searching the best threshold, the conception of memory recall position is introduced and an improved memory recall mechanism based fruit fly optimization algorithm is proposed. In the experiments, two typical classifiers: support vector machine and naive bayes are used on four datasets: Reuters50, SMSSPAS, WebKB and 20-Newsgroups, and the 10-cross validation is carried out when the measurements of F-1 and receiver operating curve are used. Experimental results show that the proposed method achieves higher accuracy than several representative traditional feature selection methods and runs faster than typical mutual information based feature selection methods, illustrating its effectiveness on achieving the best features in text classification filed.

关键词： Feature selection redundant information feature space fruit fly optimization algorithm support vector machine receiver operating curve

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The error prediction of inventory reconciliation during storage and transportation process based on PLS and MFOA-LSSVM

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JOURNAL OF PETROLEUM EXPLORATION AND PRODUCTION TECHNOLOGY 2017年第3期7卷 895-904页

作者： Shui, Aishe Fang, Hanming Zong, Fuxing Wu, Yuheng Wang, Fei Logist Engn Univ Dept Logist Informat & Logist Engn Chongqing Peoples R China Logist Engn Univ Dept Management Sci & Engn Chongqing Peoples R China

Process optimization and fault diagnosis technology, which is represented by production process monitoring, design and production condition adjustment, play an important role in the modern petroleum industry. Accurate inventory reconciliation model is the basis of process optimization and fault diagnosis. To eliminate the impact of the inventory reconciliation error caused by different metering systems, the error prediction method of inventory reconciliation during storage and transportation process based on partial least squares (PLS) and least squares support vector machine optimized by modified fruit fly optimization algorithm (MFOA-LSSVM) is proposed. The general error prediction method flow of inventory reconciliation is provided. The principles of PLS and MFOA-LSSVM are elaborated in detail. Firstly, the algorithm of PLS is used to exclude the interference of unrelated factors and extract the most relevant factors that influence the error of inventory reconciliation. Then the modified three-dimensional fruit fly optimization algorithm with diminishing steps as well as a good global search capability is adopted to select the LSSVM model parameters and build the error prediction model. Finally, the sample data were revised by using the predictive value to verify the validity of the proposed method. The experimental modeling was carried out by PLS and MFOA-LSSVM. Compared with other forecasting methods, this method not only has the advantage of faster calculations, but also can well predict the error of reconciliation.

关键词： Inventory reconciliation Error prediction Partial least squares Least squares support vector machine fruit fly optimization algorithm

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Opportunistic maintenance for wind turbines considering imperfect, reliability-based maintenance

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RENEWABLE ENERGY 2017年第Apr.期103卷 606-612页

作者： Zhang, Chen Gao, Wei Guo, Sheng Li, Youliang Yang, Tao Huazhong Univ Sci & Technol Sch Energy & Power Engn Wuhan 430074 Peoples R China

Corrective maintenance and time-based maintenance have been widely employed in wind farms. However, the actual maintenance strategy is much more complex and diverse, and also maintenance actions do not always return the system to "as good as new" condition. Moreover, dependencies exist in multi-component systems, and it is more economical to perform preventive maintenance whenever a repair opportunity presents itself. For an opportunistic maintenance strategy, when the system is shut down for maintenance, the maintenance time of other still functioning parts can be moved up to take advantage of the huge downtime losses. In this paper, an opportunistic maintenance approach is proposed for wind turbines considering an imperfect maintenance schedule that is based on reliability. A hybrid hazard rate model is used to describe imperfect maintenance and explain the effects of corrective maintenance action. Comparative examples are used to illustrate the economic advantages of opportunistic maintenance. Simulation results demonstrate the impact that various maintenance costs (fixed downtime cost and preventive replacement cost) have on the economic benefit of opportunistic maintenance strategy. The simulation analysis also provides the optimal opportunistic maintenance reliability threshold using the fruit fly optimization algorithm to provide guidance for the actual operation and maintenance of wind farms. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Wind turbine Opportunistic maintenance Reliability theory Imperfect maintenance fruit fly optimization algorithm

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STOCHASTIC FRACTAL BASED MULTIOBJECTIVE fruit fly optimization

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INTERNATIONAL JOURNAL OF APPLIED MATHEMATICS AND COMPUTER SCIENCE 2017年第2期27卷 417-433页

作者： Zuo, Cili Wu, Lianghong Zeng, Zhao-Fu Wei, Hua-Liang Hunan Univ Sci & Technol Sch Informat & Elect Engn Xiangtan 411201 Hunan Peoples R China Univ Sheffield Dept Automat Control & Syst Engn Sheffield S1 3JD S Yorkshire England

The fruit fly optimization algorithm (FOA) is a global optimization algorithm inspired by the foraging behavior of a fruit fly swarm. In this study, a novel stochastic fractal model based fruit fly optimization algorithm is proposed for multiobjective optimization. A food source generating method based on a stochastic fractal with an adaptive parameter updating strategy is introduced to improve the convergence performance of the fruit fly optimization algorithm. To deal with multiobjective optimization problems, the Pareto domination concept is integrated into the selection process of fruit fly optimization and a novel multiobjective fruit fly optimization algorithm is then developed. Similarly to most of other multiobjective evolutionary algorithms (MOEAs), an external elitist archive is utilized to preserve the nondominated solutions found so far during the evolution, and a normalized nearest neighbor distance based density estimation strategy is adopted to keep the diversity of the external elitist archive. Eighteen benchmarks are used to test the performance of the stochastic fractal based multiobjective fruit fly optimization algorithm (SFMOFOA). Numerical results show that the SFMOFOA is able to well converge to the Pareto fronts of the test benchmarks with good distributions. Compared with four state-of-the-art methods, namely, the non-dominated sorting generic algorithm (NSGA-II), the strength Pareto evolutionary algorithm (SPEA2), multi-objective particle swarm optimization (MOPSO), and multiobjective self-adaptive differential evolution (MOSADE), the proposed SFMOFOA has better or competitive multiobjective optimization performance.

关键词： multiobjective optimization fruit fly optimization algorithm stochastic fractal

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

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

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第30届中国控制与决策会议

作者： Xianxian Hou Shaohan Zhu Li Xia Gang Wu College of Computer Science and Engineering Northeastern University

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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Research on Fault Diagnosis of Servo Valve Based on Deep Learning 8

Research on Fault Diagnosis of Servo Valve Based on Deep Lea...

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2017 Prognostics and System Health Management Conference (PHM-Harbin)

作者： Chai, Jiajia Jin, Xiabo AVIC Beijing Chang Cheng Aeronaut Measurement & C Beijing Peoples R China

ISBN: (纸本)9781538603703

Aiming at some characteristics of servo valves that the complex influence between the failure modes and the high order nonlinearity of the fault datum, this paper presents a fault diagnosis model-Deep Intelligent Generalized Regression Neural Network (DGN). The DGN is a supervised deep learning model. In order to fully learning the fault datum, this paper proposed a logistic mapping and dynamic step fruit fly algorithm (lFOA-eta). Then the model introduces the improved fruit fly optimization algorithm (lFOA-eta) on the basis of the Generalized Regression Neural Network (GRNN). The lFOA-eta and GRNN are optimized to each other, forming the basis of training layer and deep training layer. The training layer and deep training layer are optimized and feedback information to each other. And finally the model structure is formed as input layer, training layer, deep training layer, and output layer. This structure improves the nonlinear approximation of the model and the convergence rate. The experimental results show that the accuracy and speed of this paper's model are improved with respect to the BP, SVM and deep belief network (DBN) model, and the application prospect is good.

关键词： fruit fly optimization algorithm Generalized Regression Neural Network deep Learning Fault Diagnosis

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Prediction Model of Dissolved Oxygen Based on FOA-LSSVR 36

Prediction Model of Dissolved Oxygen Based on FOA-LSSVR

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第36届中国控制会议

作者： Chengyun Zhu Xingqiao Liu Wangfang Ding School of Electrical and Information Engineering Jiangsu University School of New Energy and Electronic Technology Yancheng Teachers University National Engineering and Technology Center for Information Agriculture Nanjing Agriculture University

ISBN: (纸本)9781538629185

Dissolved oxygen directly affects the growth status of fishes in intensive aquaculture,thus we set up a prediction model to determine the future changing trend of dissolved *** dissolved oxygen prediction model we proposed was based on the least squares support vector regression（LSSVR） model with fruit fly optimization algorithm（FOA） to find optimal parameters（γ and σ） of *** these two parameters can significantly affect the performance of the LSSVR,we studied the other two parameter optimization methods the particle swarm optimization（PSO） algorithm,the genetic algorithm（GA） and immune genetic algorithm（IGA） to compare them with the FOA *** calculated mean absolute percentage errors of the results of the four prediction models were 0.35%,1.3%,2.03% and 1.33%,*** FOA-LSSVR model has a higher prediction accuracy and more reliable performance than the other *** the predicted values of dissolved oxygen fall below the safety level,the farmer can start an oxygen increasing machine in advance to maintain the safety of *** prediction model was used in Yangzhong,Jiangsu province,China,and it performed well and helped farmers make decisions and reduce aquaculture risks.

关键词： Dissolved oxygen prediction model least squares support vector machines fruit fly optimization algorithm

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Feature Selection using Dynamic Binary Particle Swarm optimization for Arabian Horse Identification System 12

Feature Selection using Dynamic Binary Particle Swarm Optimi...

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12th International Conference on Computer Engineering and Systems (ICCES)

作者： Zekrallah, Samar I. Soliman, Mona M. Hassanien, Aboul Ella Cairo Univ Informat Technol Dept Fac Comp & Informat Cairo Egypt Sci Res Grp Egypt Giza Egypt

ISBN: (纸本)9781538611913

Being able to formally identify horses is crucial for many reasons like biosecurity and regulatory risks, fairness in competition to ensure the proper horse and owner are competing in an event, retrieval after theft and medical record management. Among different horses types Arabian horse is one of the top ten popular horse breeds all over the world. It is the most expensive horse in the market. Such horse identification system can be based on biometric parameters. This work aims to introduce a novel method of periocular region segmentation using Otsu based method in combination with Improved fruit fly optimization algorithm (IFOA) followed by a feature extraction and selection phase for Arabian horse identification system. The segmented horse periocular region is subjected to texture analysis using Gabor filter and discrete cosine transform for proper feature extraction. A proper Feature Selection step is performed with the aim of selecting optimum features. Such optimal set of features will be used later in Arabian horse identification and recognition system. Such optimal feature selection is achieved using Dynamic Binary Particle Swarm optimization.

关键词： periocular region Biometric identification iris recognition Arabian horse identification Otsu thresholding fruit fly optimization algorithm Dynamic Binary Particle Swarm optimization

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Wind Speed Forecasting Based on EMD and GRNN Optimized by FOA

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ENERGIES 2017年第12期10卷 2001页

作者： Niu, Dongxiao Liang, Yi Hong, Wei-Chiang North China Elect Power Univ Sch Econ & Management Beijing 102206 Peoples R China Oriental Inst Technol Dept Informat Management New Taipei 220 Taiwan

As a kind of clean and renewable energy, wind power is winning more and more attention across the world. Regarding wind power utilization, safety is a core concern and such concern has led to many studies on predicting wind speed. To obtain a more accurate prediction of the wind speed, this paper adopts a new hybrid forecasting model, combing empirical mode decomposition (EMD) and the general regression neural network (GRNN) optimized by the fruit fly optimization algorithm (FOA). In this new model, the original wind speed series are first decomposed into a collection of intrinsic mode functions (IMFs) and a residue. Next, the inherent relationship (partial correlation) of the datasets is analyzed, and the results are then used to select the input for the forecasting model. Finally, the GRNN with the FOA to optimize the smoothing factor is used to predict each sub-series. The mean absolute percentage error of the forecasting results in two cases are respectively 8.95% and 9.87%, suggesting that the hybrid approach outperforms the compared models, which provides guidance for future wind speed forecasting.

关键词： wind speed forecasting empirical mode decomposition general regression neural network fruit fly optimization algorithm

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