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Design and Application of Interval Type-2 TSK Fuzzy Logic System Based on qpso algorithm

INTERNATIONAL JOURNAL OF FUZZY SYSTEMS 2018年第3期20卷 835-846页

作者： Fan, Qiu-feng Wang, Tao Chen, Yang Zhan, Zhi-feng Liaoning Univ Technol Coll Sci Jinzhou 121001 Peoples R China

Studies on interval type-2 TSK fuzzy logic system is a hot topic in the current academic area. Parameter identification is very important for system design. Commonly used parameter identification methods are the least-square algorithm, BP algorithm, etc., few scholars use qpso algorithm for parameter identification. In this paper, we propose a design of interval type-2 TSK fuzzy logic system based on quantum behaved particle swarm optimization (qpso) intelligent algorithm. Firstly, by combining the A1-C1, A2-C0, A2-C1 interval type-2 TSK fuzzy logic system with neural network, the fuzzy neural network system is designed. Then, the qpso intelligent algorithm was used to tune the fuzzy neural network system parameters, and the designed system model is applied to predict the Nasdaq Composite Index and International Gold Prices. Both qpso algorithm and BP algorithm have been used to train the system model. By comparing qpso algorithm and BP algorithm, the performance index and simulation results illustrated the proposed model is effective and feasible, which can achieve a better performance. Finally, compare the performance of the four fuzzy logic systems, it can be seen the effect of A2-C1 fuzzy logic system is better than that of the other three fuzzy logic systems.

关键词： Fuzzy logic system Neural network qpso algorithm BP algorithm Nasdaq Composite Index International Gold Prices

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Trajectory Planning of Redundant Robot Manipulators Using qpso algorithm

Trajectory Planning of Redundant Robot Manipulators Using QP...

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8th World Congress on Intelligent Control and Automation (WCICA)

作者： Guo, Jinchao Wang, Xinjin Zheng, Xiaowan Zhengzhou Inst Light Ind Henan Key Lab Information Based Elect Appliance Zhengzhou Henan Provience Peoples R China Zhengzhou Inst Light Ind Elect & Informat Engn Dept Zhengzhou Peoples R China

ISBN: (纸本)9781424467129

qpso algorithm (qpso) is introduced to design a collision-free trajectory for planar redundant manipulators. Chaotic sequences instead of random sequences are used in qpso to diversify the qpso population. Kinematics redundancy is integrated into the presented method as planning variables. Quadrinomial and quintic polynomials are used to describe the segment of the trajectory. qpso optimizes the trajectory and ensures obstacle avoidance can be achieved. Simulations are carried out for different obstacles to prove the validity of the proposed algorithm. Different test data generated by GA and qpso are provided with a tabular comparison. Simulation studies show qpso has potential online usage in engineering and distinct fast computation speed compared with GA.

关键词： Trajectory planning qpso algorithm Redundant Robot Manipulators chaotic sequences

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qpso and FISH algorithm Apply in the Wireless Sensor Network Coverage

QPSO and FISH Algorithm Apply in the Wireless Sensor Network...

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10th International Symposium on Distributed Computing and Applications to Business, Engineering and Science (DCABES)

作者： Wang Zhi Xu Wen-bo So Yangtze Univ Sch Internet Things Engn Wuxi Jiangsu Peoples R China

ISBN: (纸本)9780769544151

The applying of qpso-FISH algorithm in the wireless senor network are mainly studied in this paper. Wireless sensor network is an energy-constrain network, coverage efficiency and power consumption are two important performance indicators. Maximizing the network coverage and minimizing the number of working nodes are network optimization goals. Establish an optimal model for coverage in wireless sensor network. Utilize the characteristics of parallel, fast coveragence and fast local search speed, propose a coverage optimization strategy based on qpso-FISH algorithm. The results of simulation show that the algorithm can get an optimal set of nodes and improve the real-time capability of nodes scheduling.

关键词： wireless sensor networks AFSA algorithm qpso algorithm optimal coverage

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qpso and FISH algorithm Apply in the Wireless Sensor Network Coverage

QPSO and FISH Algorithm Apply in the Wireless Sensor Network...

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第十届电子商务、工程及科学领域的分布式计算和应用（DCABES）国际学术研讨会

作者： WANG Zhi School of Internet of Things Engineering,Southern Yangtze University,Wuxi,Jiangsu,China XU Wen-bo School of Internet of Things Engineering,Southern Yangtze University,Wuxi,Jiangsu,China

The applying of qpso-FISH algorithm in the wireless senor network are mainly studied in this *** sensor network is an energy-constrain network,coverage efficiency and power consumption are two important performance *** the network coverage and minimizing the number of working nodes are network optimization *** an optimal model for coverage in wireless sensor *** the characteristics of parallel,fast coveragence and fast local search speed,propose a coverage optimization strategy based on qpso-FISH *** results of simulation show that the algorithm can get an optimal set of nodes and improve the real-time capability of nodes scheduling.

关键词： wireless sensor networks AFSA algorithm qpso algorithm optimal coverage

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Optimization method for diagnostic sequence based on improved particle swarm optimization algorithm

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Journal of Systems Engineering and Electronics 2009年第4期20卷 899-905页

作者： Lian Guangyao Huang Kaoli Chen Jianhui Gao Fengqi Ordnance Engineering Coll. Shijiazhuang 050003 P. R. China

To realize the requirement of diagnostic sequence optimization in the process of design for testability, the authors put forward an optimization method based on quantum-behaved particle swarm optimization （qpso） algorithm. By a precedence ordering coding, the diagnostic sequence optimization can be translated into a precedence ordering problem in the multidimensional space of swarm. It can get the optimizing order quickly by using the powerful and quick search capability of qpso algorithm, and the order is the diagnostic sequence for the system. The realization of the method is simpler than other methods, and the results are more excellent than others, and it has been applied in the engineering practice.

关键词： diagnostic sequence optimization design for testability intelligent optimization qpso algorithm

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An application of preservation technology in inventory control system with price dependent demand and partial backlogging

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ALEXANDRIA ENGINEERING JOURNAL 2020年第3期59卷 1359-1369页

作者： Das, Subhash Chandra Zidan, A. M. Manna, Amalesh Kumar Shaikh, Ali Akbar Bhunia, Asoke Kumar Chandrapur Coll Dept Math Burdwan 713145 W Bengal India Univ Burdwan Dept Math Burdwan 713104 W Bengal India King Khalid Univ Coll Sci Dept Math POB 9004 Abha 61413 Saudi Arabia Al Azhar Univ Fac Sci Dept Math Assiut Branch Assiut 71524 Egypt

Preservation of a product is an important issue in the inventory control system. It prevents the deterioration effect of the products while these are stored in the warehouse/showroom. Considering deterioration effect of the product and preservation technology, an inventory model of non-instantaneous deteriorating items is developed with the demand dependent on the selling price of the product. Two different preservation rates are considered. Shortages are allowed partially with two different backlogging rates. Due to consideration of three-parameter Weibull distributed deterioration and preservation facility, the corresponding optimization problems are highly nonlinear. So, these problems cannot be solved analytically due to nonlinearity. To overcome this situation, different variants of quantum-behaved particle swarm optimization (qpso) are used. To illustrate and validate the proposed model, a numerical example is considered and solved for each case, and compared the results with the different variants of qpso algorithms. Finally, a sensitivity analysis is performed to study the effect of changes of different parameters of the model on the optimal policy. (C) 2020 The Authors. Published by Elsevier B.V. on behalf of Faculty of Engineering, Alexandria University.

关键词： Inventory Deterioration Preservation technology Partial backlogged shortage qpso algorithm

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Calibration of an agricultural-hydrological model (RZWQM2) using surrogate global optimization

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JOURNAL OF HYDROLOGY 2017年 544卷 456-466页

作者： Xi, Maolong Lu, Dan Gui, Dongwei Qi, Zhiming Zhang, Guannan Wuxi Inst Technol Dept Control Technol Wuxi 214121 Jiangsu Peoples R China Oak Ridge Natl Lab Climate Change Sci Inst Div Math & Comp Sci Oak Ridge TN 37831 USA Chinese Acad Sci Xinjiang Inst Ecol & Geog Cele Natl Stn Observat & Res Desert Grassland Eco Urumqi 830011 Peoples R China McGill Univ Dept Bioresource Engn Ste Anne De Bellevue PQ H9X 3V9 Canada Oak Ridge Natl Lab Div Math & Comp Sci Oak Ridge TN 37831 USA

Robust calibration of an agricultural-hydrological model is critical for simulating crop yield and water quality and making reasonable agricultural management. However, calibration of the agricultural-hydrological system models is challenging because of model complexity, the existence of strong parameter correlation, and significant computational requirements. Therefore, only a limited number of simulations can be allowed in any attempt to find a near-optimal solution within an affordable time, which greatly restricts the successful application of the model. The goal of this study is to locate the optimal solution of the Root Zone Water Quality Model (RZWQM2) given a limited simulation time, so as to improve the model simulation and help make rational and effective agricultural-hydrological decisions. To this end, we propose a computationally efficient global optimization procedure using sparse-grid based surrogates. We first used advanced sparse grid (SG) interpolation to construct a surrogate system of the actual RZWQM2, and then we calibrate the surrogate model using the global optimization algorithm, Quantum-behaved Particle Swarm Optimization (qpso). As the surrogate model is a polynomial with fast evaluation, it can be efficiently evaluated with a sufficiently large number of times during the optimization, which facilitates the global search. We calibrate seven model parameters against five years of yield, drain flow, and NO3-N loss data from a subsurface-drained corn-soybean field in Iowa. Results indicate that an accurate surrogate model can be created for the RZWQM2 with a relatively small number of SG points (i.e., RZWQM2 runs). Compared to the conventional qpso algorithm, our surrogate based optimization method can achieve a smaller objective function value and better calibration performance using a fewer number of expensive RZWQM2 executions, which greatly improves computational efficiency. Published by Elsevier B.V.

关键词： RZWQM2 Sparse grid Surrogate model qpso algorithm Global optimization

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Predicting S-Wave Velocity from Wire-Line Logs for Organic-Rich Rocks

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2022年第6期47卷 7261-7272页

作者： Liu, Zhishui Lu, Huan Bao, Qianzong Liu, Junzhou Yu, Hongyu Changan Univ Coll Geol Engn & Geomat Xian 710064 Shaanxi Peoples R China China Natl Offshore Oil Corp Bohai Oilfield Res Inst Tianjin 300459 Peoples R China SINOPEC Res Inst Petr Explorat & Dev Beijing 100083 Peoples R China SINOPEC Taizhou Oil Prod Plant East China Oil & Gas Co Taizhou 225300 Jiangsu Peoples R China

Lacking of S-wave velocity in logging will negatively affect the process of reservoir prediction and formation evaluation for organic rich rock reservoir. In this paper, a simple but effective method is presented to predict the S-wave velocity from P-wave velocity based on the combination of the critical porosity consolidation coefficient (CPCC) model with dual-variable parameters and quantum particle swarm optimization (qpso) algorithm. In the presented method, the P-and S-wave velocities of organic-rich rock are linked by consolidation coefficient and critical porosity. Then, the error between the measured and calculated P-wave velocity is used to establish the inverse objective function. Moreover, the qpso algorithm is introduced to invert the critical porosity and consolidation coefficient. Finally, the S-wave velocity can be predicted using the inverted parameters. Compared with two existing single-parameter adaptive methods, the predicted S-wave velocity of the proposed method is superior, which can be satisfactorily implemented for laboratory measurement and on logging data.

关键词： S-wave velocity prediction CPCC rock physics model Dual-variable parameters qpso algorithm Organic-rich rock

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qpso-optimized BP Neural Network to Predict Occurrence Quantity of Myzus persicae

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Plant Diseases and Pests 2015年第1期6卷 1-3,14页

作者： Qiu Jing Yang Yi Qin Xiyun Li Kunlin Chen Keping Yin Jianli College of Foundation and Information Engineering Yunnan Agriculture University Yunnan Tobacco Research Institute

In order to effectively predict occurrence quantity of Myzus persicae, BP neural network theory and method was used to establish prediction model for oc- currence quantity of M. persicae. Meanwhile, qpso algorithm was used to optimize connection weight and threshold value of BP neural network, so as to determine. the optimal connection weight and threshold value. The historical data of M. persica quantity in Hongta County, Yuxi City of Yunnan Province from 2003 to 2006 was adopted as training samples, and the occurrence quantities of M. persicae from 2007 to 2009 were predicted. The prediction accuracy was 99.35%, the mini- mum completion time was 30 s, the average completion time was 34.5 s, and the running times were 19. The prediction effect of the model was obviously superior to other prediction models. The experiment showed that this model was more effective and feasible, with faster convergence rate and stronger stability, and could solve the similar problems in prediction and clustering. The study provides a theoretical basis for comprehensive prevention and control against M. persicae.

关键词： BP neural network qpso algorithm Myzus persicae Occurrence quantity Prediction model

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Online Path Planning for UAV Navigation Based on Quantum Particle Swarm Optimization

Online Path Planning for UAV Navigation Based on Quantum Par...

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3rd International Conference on Teaching and Computational Science (WTCS)

作者： Guo, Jinchao Wang, Junjie Cui, Guangzhao Zhengzhou Inst Light Ind Dept Elect & Informat Engn Zhengzhou Peoples R China

ISBN: (纸本)9783642112751;9783642112768

With regard to modern warfare, the environmental information is changing and it's difficult to obtain the global environmental information in advance, so real-time flight route planning capabilities of unmanned aero vehicles (UAV) is required. Quantum Particle Swarm Optimization (qpso) is introduced to solve this optimization problem. Meanwhile, According to the threats distribution of terrain obstacles, adversarial defense radar sites and unexpected surface-to-air missile (SAM) sites, Surface of Minimum Risk (SMR) is introduced and used to form the searching space. The objective function for the proposed qpso is to minimizing traveling time and distance, while exceeding a minimum pre-defined turning radius, without collision with any obstacle in the flying workspace. Quadrinomial and quintic polynomials are used to approach the horizon projection of the 3-D route and this simplifies the original problem to a two dimension optimization problem, thus the complexity of the optimization problem is decreased, efficiency is improved. The simulation results show that this method can meet online path planning.

关键词： qpso algorithm Surface of Minimum Risk polynomial online path planning

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