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| 1 | Model predictive control and improved low-pass filtering strategies based on wind power fluctuation mitigation显示文摘The rapid development of renewable energy sources such as wind power has brought great challenges to the power grid. Wind power penetration can be improved by using hybrid energy storage(ES) to mitigate wind power fluctuation. We studied the strategy of smoothing wind power fluctuation and the strategy of hybrid ES power distribution. Firstly, an effective control strategy can be extracted by comparing constant-time low-pass filtering(CLF), variable-time low-pass filtering(VLF), wavelet packet decomposition(WPD), empirical mode decomposition(EMD) and model predictive control algorithms with fluctuation rate constraints of the identical grid-connected wind power. Moreover, the mean frequency of ES as the cutoff frequency can be acquired by the Hilbert Huang transform(HHT), and the time constant of filtering algorithm can be obtained. Then, an improved low-pass filtering algorithm(ILFA) is proposed to achieve the power allocation between lithium battery(LB) and supercapacitor(SC), which can overcome the over-charge and over-discharge of ES in the traditional low-pass filtering algorithm(TLFA). In addition, the optimized LB and SC power are further obtained based on the SC priority control strategy combined with the fuzzy control(FC) method. Finally, simulation results show that wind power fluctuation can be effectively suppressed by LB and SC based on the proposed control strategies, which is beneficial to the development of wind and storage system. | Yushu SUN Xisheng TANG Xiaozhe SUN Dongqiang JIA Zhihuang CAO Jing PAN Bin XU | 2019 | Journal of Modern Power Systems and Clean Energy2019，7，3： | 13 |
| 2 | Fuzzy-GA PID controller with incomplete derivation and its application to intelligent bionic artificial leg显示文摘An optimal PID controller with incomplete derivation is proposed based on fuzzy inference and the geneticalgorithm, which is called the fuzzy-GA PID controller with incomplete derivation. It consists of the off-line part andthe on-line part. In the off-line part, by taking the overshoot, rise time, and settling time of system unit step re-sponse as the performance indexes and by using the genetic algorithm, a group of optimal PID parameters K*p , Ti* ,and Tj are obtained, which are used as the initial values for the on-line tuning of PID parameters. In the on-linepart, based on K; , Ti* , and T*d and according to the current system error e and its time derivative, a dedicatedprogram is written, which is used to optimize and adjust the PID parameters on line through a fuzzy inference mech-anism to ensure that the system response has optimal dynamic and steady-state performance. The controller has beenused to control the D. C. motor of the intelligent bionic artificial leg designed by the authors. The result of computersimulation shows that this kind of optimal PID controller has excellent control performance and robust performance. | 谭冠政 李安平 | 2003 | Journal of Central South University of Technology2003，10，3： | 8 |
| 3 | Fuzzy cost-profit tradeoff model for locating a vehicle inspection station considering regional constraints显示文摘Facility location allocation(FLA) is one of the important issues in the logistics and transportation fields. In practice, since customer demands, allocations, and even locations of customers and facilities are usually changing, the FLA problem features uncertainty. To account for this uncertainty, some researchers have addressed the fuzzy profit and cost issues of FLA. However, a decision-maker needs to reach a specific profit, minimizing the cost to target customers. To handle this issue it is essential to propose an effective fuzzy cost-profit tradeoff approach of FLA. Moreover, some regional constraints can greatly influence FLA. By taking a vehicle inspection station as a typical automotive service enterprise example, and combined with the credibility measure of fuzzy set theory, this work presents new fuzzy cost-profit tradeoff FLA models with regional constraints. A hybrid algorithm integrating fuzzy simulation and genetic algorithms(GA) is proposed to solve the proposed models. Some numerical examples are given to illustrate the proposed models and the effectiveness of the proposed algorithm. | Guangdong TIAN Hua KE Xiaowei CHEN | 2014 | Journal of Zhejiang University-Science C(Computers and Electronics)2014，15，12： | 5 |
| 4 | A Wavelet-Based Fuzzy Neural Network for Interpolation of Fuzzy If-Then Rules显示文摘In this paper, a wavelet-based fuzzy neural network with its structure and a learningalgorithm is proposed and the simulation results are given to prove its feasibility. | Jiao Licheng Liu Fang Wang Ling & Zhang Yanning(State Key Lab. of RSP and Center for Neural Networks, Xidian University, Xi’an 710071, P. R. China) (This project was partly supported by the National Thud of Intercent. Expert and partly supported bythe | 1998 | Journal of Systems Engineering and Electronics1998，9，4： | 3 |
| 5 | A comparative study of three different learning algorithms applied to ANFIS for predicting daily suspended sediment concentration显示文摘The modeling and prediction of suspended sediment in a river are key elements in global water recourses and environment policy and management. In the present study, an Adaptive Neuro-Fuzzy Inference System model trained with the Levenberg-Marquardt learning algorithm is considered for time series modeling of suspended sediment concentration in a river. The model is trained and validated using daily river discharge and suspended sediment concentration data from the Schuylkill River in the United States. The results of the proposed method are evaluated and compared with similar networks trained with the common Hybrid and Back-Propagation algorithms, which are widely used in the literature for prediction of suspended sediment concentration. Obtained results demonstrate that models trained with the Hybrid and Levenberg-Marquardt algorithms are comparable in terms of prediction accuracy.However, the networks trained with the Levenberg-Marquardt algorithm perform better than those trained with the Hybrid approach. | Keivan Kaveh Minh Duc Bui Peter Rutschmann | 2017 | International Journal of Sediment Research2017，32，3： | 3 |
| 6 | A FUZZY REASONING PETRI NET MODEL AND ITS REASONING ALGORITHM显示文摘This paper compared the difference between the traditional Petri nets and reasoning Petri nets(RPN),and presented a fuzzy reasoning Petri net(FRPN) model to represent the fuzzy production rules of a rule based system.Based on the FRPN model,a formal reasoning algorithm using the operators in max algebra was proposed to perform fuzzy reasoning automatically.The algorithm is consistent with the matrix equation expression method in the traditional Petri net.Its legitimacy and feasibility were testified through an example. | 高梅梅 吴智铭 | 1999 | Journal of Shanghai Jiaotong university(Science)1999，4，2： | 3 |
| 7 | Fault Diagnosis Model Based on Fuzzy Support Vector Machine Combined with Weighted Fuzzy Clustering显示文摘A fault diagnosis model is proposed based on fuzzy support vector machine (FSVM) combined with fuzzy clustering (FC).Considering the relationship between the sample point and non-self class,FC algorithm is applied to generate fuzzy memberships.In the algorithm,sample weights based on a distribution density function of data point and genetic algorithm (GA) are introduced to enhance the performance of FC.Then a multi-class FSVM with radial basis function kernel is established according to directed acyclic graph algorithm,the penalty factor and kernel parameter of which are optimized by GA.Finally,the model is executed for multi-class fault diagnosis of rolling element bearings.The results show that the presented model achieves high performances both in identifying fault types and fault degrees.The performance comparisons of the presented model with SVM and distance-based FSVM for noisy case demonstrate the capacity of dealing with noise and generalization. | 张俊红 马文朋 马梁 何振鹏 | 2013 | Transactions of Tianjin University2013，19，3： | 3 |
| 8 | Multi-mode optimal fuzzy active vibration control of composite beams laminated with photostrictive actuators显示文摘Firstly, a multi-field coupling finite element formulation of composited beam laminated with the photostrictive actuators is developed in this paper. Moreover, an optimal fuzzy active control algorithm is also proposed on the basis of the combination of optimal control and fuzzy one.This method opens a new avenue to resolve the contradiction between the linear system control method and nonlinear actuating characteristics of photostrictive actuators. The desired control for suppressing multi-modal vibration of photoelectric laminated beam is firstly obtained through optimal control and then the fuzzy control is used to approach the desired mechanical strain induced by photostrictive actuators. Thus, the multi-mode vibration control of beam is realized.In the design process of optimal fuzzy controller, the design of fuzzy control is independent of optimal control. The simulation results demonstrate that the proposed control method can effectively realize multi-modal vibration control of photoelectric laminated beams, and the control effect of optimal fuzzy control is better than that of optimal state feedback control. | Mancang JIA Shijie ZHENG Rongbo HE | 2019 | Chinese Journal of Aeronautics2019，32，6： | 3 |
| 9 | Optimization of a dynamic uncertain causality graph for fault diagnosis in nuclear power plant显示文摘Fault diagnostics is important for safe operation of nuclear power plants(NPPs). In recent years, data-driven approaches have been proposed and implemented to tackle the problem, e.g., neural networks, fuzzy and neurofuzzy approaches, support vector machine, K-nearest neighbor classifiers and inference methodologies. Among these methods, dynamic uncertain causality graph(DUCG)has been proved effective in many practical cases. However, the causal graph construction behind the DUCG is complicate and, in many cases, results redundant on the symptoms needed to correctly classify the fault. In this paper, we propose a method to simplify causal graph construction in an automatic way. The method consists in transforming the expert knowledge-based DCUG into a fuzzy decision tree(FDT) by extracting from the DUCG a fuzzy rule base that resumes the used symptoms at the basis of the FDT. Genetic algorithm(GA) is, then, used for the optimization of the FDT, by performing a wrapper search around the FDT: the set of symptoms selected during the iterative search are taken as the best set of symptoms for the diagnosis of the faults that can occur in the system. The effectiveness of the approach is shown with respect to a DUCG model initially built to diagnose 23 faults originally using 262 symptoms of Unit-1 in the Ningde NPP of the China Guangdong Nuclear Power Corporation. The results show that the FDT, with GA-optimized symptoms and diagnosis strategy, can drive the construction of DUCG and lower the computational burden without loss of accuracy in diagnosis. | Yue Zhao Francesco Di Maio Enrico Zio Qin Zhang Chun-Ling Dong Jin-Ying Zhang | 2017 | Nuclear Science and Techniques2017，28，3： | 2 |
| 10 | Intelligent modeling and optimization on time-sharing power dispatching system for electrolytic zinc process显示文摘Based on real time price counting of electric power, an optimization model of time sharing power for electrolytic zinc process(EZP) was established by means of an incremental fuzzy neural network(FNN), which is adopted to approximate the relationship of current efficiency, current density and acidity. Penalty function introduced and optimal objective function reconstructed, a single loop simulated annealing algorithm(SAA) by using mutation and extending searching spaces was used to obtain optimal time sharing power scheme. Industrial practical results show that the whole system can greatly decrease the power consumption of EZP and increase the time sharing profits. | 王雅琳 桂卫华 阳春华 黄泰松 | 2000 | 中国有色金属学会会刊:英文版2000，10，4： | 2 |
| 11 | Optimized Mamdani fuzzy models for predicting the strength of intactrocks and anisotropic rock masses显示文摘Development of accurate and reliable models for predicting the strength of rocks and rock masses is one of the most common interests of geologists,civil and mining engineers and many others.Due to uncertainties in evaluation of effective parameters and also complicated nature of geological materials,it is difficult to estimate the strength precisely using theoretical approaches.On the other hand,intelligent approaches have attracted much attention as novel and effective tools of solving complicated problems in engineering practice over the past decades.In this paper,a new method is proposed for mining descriptive Mamdani fuzzy inference systems to predict the strength of intact rocks and anisotropic rock masses containing well-defined through-going joint.The proposed method initially employs a genetic algorithm(GA)to pick important rules from a preliminary rule base produced by grid partitioning and,subsequently,selected rules are given weights using the GA.Moreover,an information criterion is used during the first phase to optimize the models in terms of accuracy and complexity.The proposed hybrid method can be considered as a robust optimization task which produces promising results compared with previous approaches. | Mojtaba Asadi | 2016 | Journal of Rock Mechanics and Geotechnical Engineering2016，8，2： | 1 |
| 12 | A Fuzzy Decision Based WSN Localization Algorithm for Wise Healthcare显示文摘Wise healthcare is a typical application of wireless sensor network(WSN), which uses sensors to monitor the physiological state of nursing targets and locate their position in case of an emergency situation. The location of targets need to be determined and reported to the control center,and this leads to the localization problem. While localization in healthcare field demands high accuracy and regional adaptability, the information processing mechanism of human thinking has been introduced,which includes knowledge accumulation, knowledge fusion and knowledge expansion. Furthermore, a fuzzy decision based localization approach is proposed. Received signal strength(RSS) at references points are obtained and processed as position relationship indicators, using fuzzy set theory in the knowledge accumulation stage; after that, optimize degree of membership corresponding to each anchor nodes in different environments during knowledge fusion; the matching degree of reference points is further calculated and sorted in decision-making, and the coordinates of several points with the highest matching degree are utilized to estimate the location of unknown nodes while knowledge expansion. Simulation results show that the proposed algorithm get better accuracy performance compared to several traditional algorithms under different typical occasions. | Jiangyu Yan Ran Qiao Liangrui Tang Chenxi Zheng Bing Fan | 2019 | China Communications2019，16，4： | 1 |
| 13 | Soft-sensing modeling and intelligent optimal control strategy for distillation yield rate of atmospheric distillation oil refining process显示文摘It is a challenge to conserve energy for the large-scale petrochemical enterprises due to complex production process and energy diversification. As critical energy consumption equipment of atmospheric distillation oil refining process, the atmospheric distillation column is paid more attention to save energy. In this paper, the optimal problem of energy utilization efficiency of the atmospheric distillation column is solved by defining a new energy efficiency indicator - the distillation yield rate of unit energy consumption from the perspective of material flow and energy flow, and a soft-sensing model for this new energy efficiency indicator with respect to the multiple working conditions and intelligent optimizing control strategy are suggested for both increasing distillation yield and decreasing energy consumption in oil refining process. It is found that the energy utilization efficiency level of the atmospheric distillation column depends closely on the typical working conditions of the oil refining process, which result by changing the outlet temperature, the overhead temperature, and the bottom liquid level of the atmospheric pressure tower. The fuzzy C-means algorithm is used to classify the typical operation conditions of atmospheric distillation in oil refining process. Furthermore, the LSSVM method optimized with the improved particle swarm optimization is used to model the distillation rate of unit energy consumption. Then online optimization of oil refining process is realized by optimizing the outlet temperature, the overhead temperature with IPSO again. Simulation comparative analyses are made by empirical data to verify the effectiveness of the proposed solution. | Zheng Wang Cheng Shao Li Zhu | 2019 | Chinese Journal of Chemical Engineering2019，27，5： | 1 |
| 14 | Fuzzy Fractional-Order Fast Terminal Sliding Mode Control for Some Chaotic Microcomponents显示文摘In this paper,we propose a novel fractionalorder fast terminal sliding mode control method,based on an integer-order scheme,to stabilize the chaotic motion of two typical microcomponents.We apply the fractional Lyapunov stability theorem to analytically guarantee the asymptotic stability of a system characterized by uncertainties and external disturbances.To reduce chattering,we design a fuzzy logic algorithm to replace the traditional signum function in the switching law.Lastly,we perform numerical simulations with both the fractional-order and integer-order control laws.Results show that the proposed control law is effective in suppressing chaos. | Jianxin Han Qichang Zhang Wei Wang Jing Wang | 2017 | Transactions of Tianjin University2017，23，3： | 1 |
| 15 | Integrated parallel forecasting model based on modified fuzzy time series and SVM显示文摘A dynamic parallel forecasting model is proposed,which is based on the problem of current forecasting models and their combined model. According to the process of the model, the fuzzy C-means clustering algorithm is improved in outliers operation and distance in the clusters and among the clusters. Firstly,the input data sets are optimized and their coherence is ensured,the region scale algorithm is modified and non-isometric multiscale region fuzzy time series model is built. At the same time,the particle swarm optimization algorithm about the particle speed,location and inertia weight value is improved, this method is used to optimize the parameters of support vector machine, construct the combined forecast model, build the dynamic parallel forecast model, and calculate the dynamic weight values and regard the product of the weight value and forecast value to be the final forecast values. At last, the example shows the improved forecast model is effective and accurate. | Yong Shuai Tailiang Song Jianping Wang | 2017 | Journal of Systems Engineering and Electronics2017，28，4： | 1 |
| 16 | Advanced Fuzzy C-Means Algorithm Based on Local Density and Distance显示文摘This paper presents an advanced fuzzy C-means(FCM) clustering algorithm to overcome the weakness of the traditional FCM algorithm, including the instability of random selecting of initial center and the limitation of the data separation or the size of clusters. The advanced FCM algorithm combines the distance with density and improves the objective function so that the performance of the algorithm can be improved. The experimental results show that the proposed FCM algorithm requires fewer iterations yet provides higher accuracy than the traditional FCM algorithm. The advanced algorithm is applied to the influence of stars' box-office data, and the classification accuracy of the first class stars achieves 92.625%. | 吴绍春 庞毅杰 邵森 江科元 | 2018 | Journal of Shanghai Jiaotong university(Science)2018，23，5： | 1 |
| 17 | Strategies for Optimizing Feed Rate of Fed-Batch Yeast Fermentation by Fuzzy-Neural Network显示文摘In this paper,a novel fuzzy neural network model,in which an adjustable fuzzy sub-space was designed by uniform design,has been established and used in fed-batch yeast fermentationas an example.A brand-new optimization sub-network with special structure has been built andgenetic algorithm,guaranteeing the optimization in overall space,is introduced for the feed rateoptimization.On the basis of the model network,the optimal substrate concentration and theoptimal amount of fed-batch at different periods have been studied,aided with the optimizationnetwork and the genetic algorithm separately.The above results can be used as a basis for theestablishment of a fuzzy neural network controller. | 苗志奇 元英进 | 1998 | Chinese Journal of Chemical Engineering1998，6，4： | 1 |
| 18 | Diagnostic methods and risk analysis based on fuzzy soft information显示文摘In our daily life problem we face uncertainties in making right decisions. In this study, we propose two different decision-making problems in medical field. The first problem is fever diagnosing and second problem is mouth cancer risk analysis. In the first problem, we use fuzzy soft similarity measures and fuzzy soft matrix operations to diagnose the type of fever. We consider a hypothetical case study and manipulate similarity measures on it. Our work diagnoses different patients having similar symptoms. We also develop a small application using JAVA. In the second problem, we perform risk analysis of mouth cancer. The proposed fuzzy soft expert system takes two biochemical parameters as inputs that is, serum total malondialdehyde (MDA), and serum proton donors capacity (donors_protons) and determines the risk of mouth cancer. Our study facilitates doctors by diagnosing mouth cancer at its earlier stages. There are four main components of our fuzzy soft expert system. The first component is named as fuzzification which converts crisp input into linguistic variables and formulates fuzzy sets. The second component transforms fuzzy sets into their respective fuzzy soft sets. The third coinponent determines indispensable parameters and perforins parameter reduction. The fourth component performs risk analysis by using algorithm. We use exemplary dataset and run all the components of fuzzy soft expert system to compute cancer risk. | Shaista Habib Muhammad Akrarn | 2018 | International Journal of Biomathematics2018，11，8： | 1 |
| 19 | An Adaptive Simulation Algorithm Based on Fuzzy Stiffness Recognition and Its Application to Power Station Simulation显示文摘AnAdaptiveSimulationAlgorithmBasedonFuzzyStifnesRecognitionandItsApplicationtoPowerStationSimulationWangRuifangAutomationDepa... | Wang Ruifang Automation Department, Chongqing University, 630044, P. R. China Chen Rui Research and Training Center of Locomotive Driving Simulation, Southwest Jiaotong University, Chengdu, P. R. China Xu Song Automation Department, Chongqi | 1998 | Journal of Systems Engineering and Electronics1998，9，1： | 1 |
| 20 | Evolved Algorithm and Vibration Stability for Nonlinear Disturbed Security Systems显示文摘In this paper,a method sustaining system stability after decomposition is proposed.Based on the stability criterion derived from the energy function,a set of intelligent controllers is synthesized which is used to maintain the stability of the system.The sustainable stability problem can be reformulated as a Linear Matrix Inequalities(LMI)problem.The key to guaranteeing the stability of the system as a whole is to find a common symmetrically positive definite matrix for all subsystems.Furthermore,the Evolved Bat Algorithm(EBA)is employed to replace the pole assignment method and the conventional mathematical methods for solving the LMI.The EBA is utilized to find feasible solutions in terms of the energy equation.The experimental results show that the EBA is capable of providing proper solutions,which satisfy the sustainability and stability criteria,after a short period of recursive computing. | Tcw Chen Wray Marriott Ann Nicholson Tim Chen Mars Kmieckowiak Jcy Chen | 2019 | Sound & Vibration2019，53，2： | 1 |
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