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The application of SOFNN based on pso-ILM algorithm in nonlinear system modeling

APPLIED INTELLIGENCE 2023年第8期53卷 8927-8940页

作者： Deng, Huaijun Liu, Linna Fang, Jianyin Yan, Li Zhongyuan Univ Technol Sch Elect & Informat Zhengzhou 450007 Peoples R China

Fuzzy neural network (FNN) is the product of the combination of fuzzy theory and neural network. It combines the advantages of neural network and fuzzy theory, which has achieved great success in various fields. However, on the one hand, when the practical input is intricate and high dimensional data, the existing FNN can't achieve a good modeling effect in the speed of convergence, the accuracy of modeling and generalization ability. On the other hand, the number of rules in FNN is fixed, which will also lead to the above problems in nonlinear system modeling. In this paper, a self-organizing fuzzy neural network based on Particle Swarm Optimization with improved Levenberg-Marquardt learning algorithm (SOFNN-pso-ILM) is proposed for nonlinear system modeling. First, SOFNN based on pso-ILM is built online by a method of constantly learning parameters and structures. In the process of structures learning, the number of fuzzy rules that have been set can be self-designed with the growing and pruning algorithm, which is based on the size of the singular value. In the process of parameters learning, pso algorithm combined with ILM algorithm is used to update parameters. Then, the convergence and stability of SOFNN based on pso-ILM are analyzed. Finally, the proposed method is used to model in the nonlinear system by three examples. The modeling results demonstrate that the proposed SOFNN based on pso-ILM can model in nonlinear systems effectively.

关键词： Fuzzy neural network Self-organizing learning pso algorithm LM algorithm

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A Method for power system state assessment and analysis based on pso algorithm 22

A Method for power system state assessment and analysis base...

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Proceedings of the 2022 5th International Conference on algorithms, Computing and Artificial Intelligence

作者： Wei Zhou Xinlei Cai Jinzhou Zhu Xu Lin Xinglang Xie Power Dispatching and Controlling Center of Guangdong Power Grid Company Limited Guangdong Power Grid Company Limited China Digital Grid Branch of China Southern Power Grid Digital Power Grid Group Co. Ltd China

ISBN: (纸本)9781450398336

Aiming at the problem of large error in power system assessment results, this paper proposes a power system state assessment and analysis method based on pso algorithm. Firstly, the existing assessment and analysis methods for power system state are analyzed, and then the state assessment and analysis model for regional power system is established to determine the corresponding assessment principles. Then, pso algorithm is used to solve the optimal strategy of power system state assessment. Finally, the performance of the proposed method is verified and analyzed based on MATLAB software platform. The experimental results show that the assessment and analysis method for power system state can quickly determine the optimal strategy of power system state assessment, effectively improve the accuracy of power system state assessment, and help to maintain the safe and stable operation of power system.

关键词： pso algorithm state assessment power system analysis model optimal strategy

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Study on Greenhouse Environment Neural Network Model Based on pso algorithm

Study on Greenhouse Environment Neural Network Model Based o...

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2010 International Conference on Intelligent Computing and Cognitive Informatics (ICICCI 2010)

作者： Bin Zhao1 2 1)College of Electromechanical Engineering Northeast Forestry University Harbin,Heilongjiang Province,China2) College of Information Technology Heilongjiang Bayi Agricultural University Daqing,Heilongjiang Province,ChinaKeqi Wang1 2) College of Information Technology Heilongjiang Bayi Agricultural University Daqing,Heilongjiang Province,China

Greenhouse environment models easily fitted strong noise data,and its' generalization *** this paper,ROLS(Regularized Orthogonal Least Squares) algorithm effectively decreased the influence of noise data,and automatically designed smaller NN ***(Particle Swarm Optimization) algorithm optimized the parameters of *** was experimented with spring environment data of northern greenhouse in *** results show:compared with model based on OLS algorithm,this model is of smaller NN structure,mean error of temperature and humidity respectively decreases 0.0008 ℃ and 0.0004%RH,this model is better on approximation and *** model is beneficial to design control scheme and structure of the northern greenhouse.

关键词： greenhouse model neural network pso algorithm ROLS algorithm

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Time-optimal Trajectory Planning of Dulcimer Music Robot Based on pso algorithm

Time-optimal Trajectory Planning of Dulcimer Music Robot Bas...

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第32届中国控制与决策会议

作者： Weimin Zhang Shixiong Fu School of Mechanical and Electronic Information China University of Geosciences

For a dulcimer music robot that strikes a dulcimer,fast and high-frequency striking is a key requirement,so the time optimization of the trajectory planning of the robot arm is the first problem to be solved in motion *** this paper,the kinematics modeling and solving are firstly completed by using the D-H parameter *** the steps of trajectory planning by interpolation and the application of basic pso algorithm are *** for the deficiency of only velocity constraint,we propose an improve pso algorithm considering complete kinematic constraints of all joints,which can ensure that the optimal trajectory planned is ***,the simulation experiments of time optimization and trajectory planning are carried out in *** show that after optimizing the interpolation time by the improved pso algorithm under the kinematic constraints,the total time of trajectory planning of the robot is greatly reduced by 74.35%;and the planned time-optimal trajectory curves are continuous and smooth,which meets the requirements of motion stability of the *** a result,the efficiency of the improved pso algorithm applied to timeoptimal trajectory planning of robot is verified.

关键词： Dulcimer Music Robot Time-optimal Trajectory Planning pso algorithm

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Applying Heterogeneous Cooperative Relay Network Based Cooperative Relay Node Selection and pso algorithm

Applying Heterogeneous Cooperative Relay Network Based Coope...

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2019 International Conference on Applied Mathematics, Modeling, Simulation and Optimization(AMMSO 2019)

作者： Guang-yu WU School of Information Technology and Engineering Guangzhou College of Commerce

In this paper a novel scheme of cooperative networks depending on the number and locations of relays in the network. The effect of relay number and locations are investigated by considering energy optimization. First selects the optimal relay from a set of available relays and then uses this "optimal" relay for cooperation between the source and the destination. The simulation-based performance analysis confirms that the cooperative relaying scheme has an advantage of diversity gain thus improving the bit error ratio performance. The simulation results demonstrate that the proposed cooperative relay node selection algorithm can improve performances by achieving the cooperative gain.

关键词： pso algorithm Cooperative Relay Network Cooperative networks

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An Improved Interval pso algorithm with Dynamic Shrinking

An Improved Interval PSO Algorithm with Dynamic Shrinking

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第28届中国控制与决策会议

作者： Shou-ping Guan You-dong Zhang College of Information Science and Engineering Northeastern University

ISBN: (纸本)9781467397155

Considering the slow convergence problem of conventional interval particle swarm optimization algorithm based on static shrinking strategy(SIpso),this paper proposes a new dynamic shrinking strategy to form a new interval particle swam optimization algorithm(DIpso) to improve the SIpso,which can make the interval shrinking more flexible and be conducive to the quick *** simulation results show that the efficiency of DIpso is superior to SIpso,and demonstrate the improvement for SIpso is effective

关键词： Interval optimization pso algorithm Dynamic shrinking

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Application of pso algorithm to Multi-objective Optimization in Network Planning

Application of PSO Algorithm to Multi-objective Optimization...

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2012 International Conference on Computer Science and Artificial Intelligence(CSAI 2012)

作者： Lixin Qu Yihong Zhou Na Chen School of Water Resources and Hydropower Wuhan University School of Business Hohai University

In this paper, we apply well-performed Particle Swarm Optimization (pso) algorithm to solve multi-objective optimization problem in engineering network planning, and propose possible initialization and constraints by multi-constrained matrix and heuristic criterion to reduce complexity of penalty factor. In the coding process, we define the particle, level objectives by importance, and build linear weighted method by fitness function of particles to resolve the partial ordering problem derived from multi-objective comparison. Numerical result indicates the proposed approach can increase the solution speed, reduce the solution difficulty of pso algorithm and search space about its solution. And it also demonstrates pso algorithm is practical and valid in multi-objective network optimization.

关键词： pso algorithm multi-objective optimization network planning

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Cloud Data Migration Method Based On pso algorithm

Cloud Data Migration Method Based On PSO Algorithm

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第十四届分布式计算及其应用国际学术研讨会

作者： Geng Yushui Yuan Jiaheng School of Information Qilu University Of Technology

Cloud storage system can play an important role in large-scale, and it supports high-performance cloud applications. To cloud storage systems, data migration is key technology to realize the nodes dynamically extensible and elastic load balancing. How to reduce migration cost of time is the problem that cloud service providers need to solve. Existing research efforts were focused on the data migration issues under the non-virtualized environments, which often do not applicable to cloud storage systems. In response to these challenges, we put data migration issues into the loadbalancing scenarios to solve. We propose an algorithm based on particle swarm optimization algorithm which can reduces the cost of time. In the experiment, we can use Yahoo services benchmarking YCSB tool which could verify the validity of the method. It is a test framework designed to help users understand the different cloud computing, database performance.

关键词： Cloud data Data migration pso algorithm Load balance

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A Federated Learning Model for Fault Diagnosis of IIoT Using a Modified pso algorithm Customized by Taguchi Method 23

A Federated Learning Model for Fault Diagnosis of IIoT Using...

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Proceedings of the 2023 5th International Conference on Internet of Things, Automation and Artificial Intelligence

作者： Huiqi Zhao Yulong Guo Min Wang Fang Fan Huajie Zhang Yaowen Ma College of Intelligent Equipment Shandong University of Science and Technology China Guoshen Company Technical Support Center China

ISBN: (纸本)9798400716485

The Industrial Internet of Things (IIoT) represents the deployment of Internet of Things (IoT) technology in industrial applications. In this article, we address the challenges of fault diagnosis and data privacy protection within the IIoT environment. We present a novel fault diagnosis model that combines federated learning and a particle swarm optimization algorithm. Firstly, we introduce a three-tier federated learning model designed to safeguard the data privacy of each participant in a real industrial control network structure. Subsequently, we enhance the particle swarm optimization algorithm to augment its global exploration capabilities and convergence performance, enabling it to collect federated learning model weights in lieu of traditional techniques. Furthermore, we employ the Taguchi method to tailor an optimal solution for the modified particle swarm optimization algorithm (TMpso), thereby enhancing the algorithm's efficiency and robustness. Additionally, we propose a neural network model utilizing small convolutional kernels (SVGG) for fault diagnosis within the IIoT framework, thereby improving the model's feature learning capabilities. Experimental validation was conducted using actual industrial rolling bearing datasets and CIFAR-10 datasets. The results of these experiments demonstrate that our proposed TPMpso-SVGG model outperforms other methods in terms of fault identification accuracy and communication cost.

关键词： Federated learning(FL) Industrial Internet of Things(IIoT) pso algorithm fault diagnosis taguchi method

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A Parameters Identification Method for Hammerstein Systems Based on pso algorithm

A Parameters Identification Method for Hammerstein Systems B...

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第27届中国控制与决策会议

作者： Rui Wang Zhizhong Mao College of Information Science and Engineering Northeastern University

ISBN: (纸本)9781479970186

The non-linear static components of Hammerstein systems are described by method of polynomial and piecewise linearization ***,parameters which need to identify through mathematical derivation can be *** pso algorithm is used to identify Hammerstein system *** simulations demonstrate that parameters identification method based on pso algorithm has strong robustness to measuring noise and uncertain models.

关键词： Hammerstein system Parameter identification pso algorithm

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