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pso type-reduction method for geometric interval type-2 fuzzy logic systems

Journal of Harbin Institute of Technology(New Series) 2008年第6期15卷 862-867页

作者：赵先章高一波曾隽芳杨一平 Institute of Automation Chinese Academy of Sciences China Agricultural University

In a special case of type-2 fuzzy logic systems （FLS）, i.e. geometric inteIval type-2 fuzzy logic systems （GIT-2FLS）, the crisp output is obtained by computing the geometric center of footprint of uncertainly （FOU） without type-reduction, but the defuzzifying method acts against the corner concepts of type-2 fuzzy sets in some cases. In this paper, a pso type-reduction method for GIT-2FLS based on the particle swarm optimization （pso） algorithm is presented. With the pso type-reduction, the inference principle of geometric interval FLS operating on the continuous domain is consistent with that of traditional interval type-2 FLS operating on the discrete domain. With comparative experiments, it is proved that the pso type-reduction exhibits good performance, and is a satisfactory complement for the theory of GIT-2FLS.

关键词： interval type-2 fuzzy sets pso algorithm type-reduction

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A modified pso learning algorithm for PID neural network

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25th Chinese Control Conference

作者： Li Ming Yang Chengwu Nanjing Univ Sci & Technol Coll Power Engn Nanjing 210094 Peoples R China

ISBN: (纸本)9787810778022

Traditional PED neural network adopts BP learning algorithm. However, without accurate gradients, its initial MSE is too large and the procedure of convergence may be unstable. A modified pso (Mpso) algorithm is introduced to training the PID neural network. The WSO algorithm does not need any gradient information. It can keep large variety all along and solve premature convergence, which is a major problem in basic pso algorithm. Simulation results show Mpso algorithm is the best learning algorithm for PID neural network.

关键词： PID neural network pso algorithm learning algorithm

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A Multi-swarm pso and its Application in Operational Optimization of Ethylene Cracking Furnace

A Multi-swarm PSO and its Application in Operational Optimiz...

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7th World Congress on Intelligent Control and Automation

作者： Geng, Zhiqiang Zhu, Qunxiong Beijing Univ Chem Technol Coll Informat Sci &Technol Beijing 100029 Peoples R China

ISBN: (纸本)9781424421138

The exiting pso algorithms are analyzed deeply, a multi-swarm pso (MSpso) is studied. The whole swarm is divided into three sub-swarms randomly, the first particle group obeys the standard pso principle to search the optimal result, the second searches randomly inner neighborhood of the optimal result, the third does not care about the optimal result but flies freely according to themselves velocities and positions. So the algorithm enhances its global searching space, enriches particles' diversity in order to let particles jump out local optimization points. Testing and comparing results with standard pso and linearly decreasing weight pso by several widely used benchmark functions show optimization performance of the algorithm is better. Furthermore, the proposed algorithm is employed to resolve the operational optimization problems of ethylene cracking furnace. The operational optimization results for built cracking model are effective and satisfying.

关键词： pso algorithm Ethylene cracking furnace Operational optimization

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Product Module Identification Based on pso

Product Module Identification Based on PSO

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International Symposium on Information Science and Engineering

作者： Shan, Quan Chen, Yan Yan, Guangrong Northeast Univ Qinhuangdao Automat Engn Dept Qinhuangdao 066004 Peoples R China Beihang Univ Sch Mech Engn Automat Beijing 1000083 Peoples R China

ISBN: (纸本)9780769534947

A numerical optimization method for module identification based on particle swarm optimization (pso) algorithm is proposed. A series of property correlations facing the product lifecycle are divided into functional, geometrical, physical and auxiliary ones, and the synthesis design structure matrix (DSM) is obtained. The optimization function for module identification is then established based on the principle of axiomatic design (i.e., close cohesion and loose coupling). Finally, the numerical optimization method for product module identification is implemented using pso. Solutions is provided for some key issues such as constructing the integrated relationship matrix, building the objective optimization function, creating the numerical optimization process and encoding schema. An example is presented to demonstrate the feasibility of the proposed approaches.

关键词： axiomatic theory module identification pso algorithm

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A search for maximum efficiency and NO_x reduction through spark-ignition engine design optimization at λ=1

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING 2008年第D5期222卷 875-886页

作者： Mehrani, P. Watson, H. C. Univ Melbourne Dept Mech Engn Parkville Vic 3010 Australia

In this study a multizone model of spark-ignition engine combustion is validated and used to predict the thermal efficiency and NOx emissions of the engine. The model is validated against an engine map obtained from an extensive series of experiments. An optimizing algorithm based on particle swarm concepts is applied to the model to find a tradeoff between efficiency and NOx emissions using a predefined cost function. Optimization is performed for three cases, each of which progressively includes more variables for optimization. A further constrained optimization case is performed with constant values of several of the variables set at the average values found in the three prior cases. These variables are the ones that change over a small domain. The results show the potential improvement in efficiency while achieving remarkably low NOx emissions. They emphasize the importance of exhaust gas recirculation (EGR) at high compression ratio and high loads when the maximum in-cylinder pressures are very high. They also suggest some strategies for valve timings that use internal EGR (residual gas) and move towards different compression and expansion ratios (Atkinson cycle).

关键词： maximum thermal efficiency NOx reduction spark-ignition engine optimizing algorithm exhaust gas recirculation pso algorithm residual fraction

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Using pso algorithm to evolve an optimum input subset for a SVM in time series forecasting

Using PSO algorithm to evolve an optimum input subset for a ...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Zhang, CK Hu, H Harbin Inst Technol Shenzhen Grad Sch Mech Engn & Automat Dept Shenzhen Peoples R China

ISBN: (纸本)0780392981

Using particle swarm optimization (pso) algorithm to evolve an optimum input subset for a SVM is proposed. Binary pso algorithm is employed in feature selection, in which each particle represented as a binary vector corresponds to a candidate input subset. A swarm of particles flies through the input set space for targeting the optimal subset. In order to evaluate the reasonable fitness of each input subset, pso algorithm is used to adaptively evolve SVM to obtain the best performance of network, in which each particle represented as a real vector corresponds to the candidate kernel parameters Of SVM. This method has been applied in a real financial time series forecasting, the results show that it has better performance of generalization, and higher rate of convergence.

关键词： pso algorithm optimum input time series forecasting

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Weak signal detection based on a new Matching Pursuit method

Weak signal detection based on a new Matching Pursuit method

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7th International Conference on Machine Learning and Cybernetics

作者： Xu, Gang Gao, Jie N China Elect Power Univ Dept Elect & Elect Engn Beijing 102206 Peoples R China

ISBN: (纸本)9781424420957

In this paper, the theory of sparse decomposition is introduced to weak signal detection, and the improved Matching Pursuit (MP) algorithm is studied to accomplish anti- interference process of sonic typical signals, such as a weak sine wave signal submerged in strong noise. Given that the traditional NIP algorithm has a large number of calculations, the novel Particle Swarm Optimization (pso) algorithm is used to improve the efficiency of searching for time-frequency atoms, thereby achieving high search efficiency of time-frequency atoms and rapid noise restraint. The results or experiments indicated that the improved algorithm can effectively increase the search speed by approximately 100 times and reduce the noises above Signal to Noise Ratio (SNR) -15.

关键词： MP algorithm pso algorithm signal detection wavelet transformation

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Synthesis of a Hybrid Five-Bar Mechanism with Particle Swarm Optimization algorithm

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5th International Symposium on Neural Networks

作者： Zhang, Ke Shanghai Inst Technol Sch Mech & Automat Engn Shanghai 200235 Peoples R China

ISBN: (纸本)9783540877318

Hybrid mechanism is a new type of mechanism with flexible transmission behavior. Hybrid five-bar mechanism is the most representative one of them. In this paper, modeling and analysis for a hybrid Five-bar mechanism based on power bond graph theory is introduced. An optimal dimensional synthesis of hybrid mechanism is performed with reference to dynamics objective function. Compared with conventional optimum evaluation methods such as simplex search and Powell method, Particle Swarm Optimization (pso) algorithm can improve the efficiency of searching in the whole field by gradually shrinking the area of optimization variable. Compared to GA, pso is easy to implement and there are few parameters to adjust. In order to solve the synthesis problem, integrating pso optimization algorithm and MATLAB Optimization Toolbox for the constraint equations. Optimum link dimensions are obtained assuming there are no dimensional tolerances or clearances. Finally, a numerical example is carried out, and the Simulation results show that the optimization method is feasible and satisfactory for hybrid mechanism.

关键词： Synthesis mechanism optimization pso algorithm

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WEAK SIGNAL DETECTION BASED ON A NEW MATCHING PURSUIT METHOD

WEAK SIGNAL DETECTION BASED ON A NEW MATCHING PURSUIT METHOD

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2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)

作者： GANG XU JIE GAO Department of Electrical and Electronic Engineering North China Electric Power University Beijing 102206China

In this paper, the theory of sparse decomposition is introduced to weak signal detection, and the improved Matching Pursuit (IMP) algorithm is studied to accomplish anti- interference process of some typical signals, such as a weak sine wave signal submerged in strong noise. Given that the traditional MP algorithm has a large number of calculations, the novel Particle Swarm Optimization (pso) algorithm is used to improve the efficiency of searching for time-frequency atoms, thereby achieving high search efficiency of time-frequency atoms and rapid noise restraint. The results of experiments indicated that the improved algorithm can effectively increase the search speed by approximately 100 times and reduce the noises above Signal to Noise Ratio (SNR) -15.

关键词： MP algorithm pso algorithm Signal detection Wavelet transformation

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Random fuzzy EOQ model with imperfect quality items

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FUZZY OPTIMIZATION AND DECISION MAKING 2007年第2期6卷 139-153页

作者： Wang, Xiobin Tang, Wansheng Zhao, Ruiquing Tianjin Univ Inst Syst Engn Tianjin 300072 Peoples R China Shandong Univ Finance Sch Comp & Informat Engn Shandong 250014 Peoples R China

This paper investigates an economic order quantity (EOQ) problem with imperfect quality items, where the percentage of imperfect quality items in each lot is characterized as a random fuzzy variable while the setup cost per lot, the holding cost of each unit item per day, and the inspection cost of each unit item are characterized as fuzzy variables, respectively. In order to maximize the expected long-run average profit, a random fuzzy EOQ model is constructed. Since it is almost impossible to find an analytic method to solve the proposed model, a particle swarm optimization (pso) algorithm based on the random fuzzy simulation is designed. Finally, the effectiveness of the designed algorithm is illustrated by a numerical example.

关键词： inventory fuzzy variable random fuzzy variable pso algorithm

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