Modifying the velocity in adaptive PSO to improve optimisation performance

2017 ◽  
Author(s):  
George Tambouratzis
Keyword(s):  
Adaptive Pso

scholarly journals An Associate Rules Mining Algorithm Based on Artificial Immune Network for SAR Image Segmentation

2015 ◽  
Vol 2015 ◽  
pp. 1-14
Author(s):  
Mengling Zhao ◽  
Hongwei Liu
Keyword(s):  
Association Rules ◽  
Large Scale ◽  
Classification Algorithm ◽  
Data Sets ◽  
Immune Network ◽  
Artificial Immune ◽  
Mining Algorithm ◽  
Sar Image Segmentation ◽  
Artificial Immune Network ◽  
Adaptive Pso

As a computational intelligence method, artificial immune network (AIN) algorithm has been widely applied to pattern recognition and data classification. In the existing artificial immune network algorithms, the calculating affinity for classifying is based on calculating a certain distance, which may lead to some unsatisfactory results in dealing with data with nominal attributes. To overcome the shortcoming, the association rules are introduced into AIN algorithm, and we propose a new classification algorithm an associate rules mining algorithm based on artificial immune network (ARM-AIN). The new method uses the association rules to represent immune cells and mine the best association rules rather than searching optimal clustering centers. The proposed algorithm has been extensively compared with artificial immune network classification (AINC) algorithm, artificial immune network classification algorithm based on self-adaptive PSO (SPSO-AINC), and PSO-AINC over several large-scale data sets, target recognition of remote sensing image, and segmentation of three different SAR images. The result of experiment indicates the superiority of ARM-AIN in classification accuracy and running time.

PSS with SVC Damping Controllers Coordinated Design and Real-Time Implementation in Multi-Machine Power System Using Advanced Adaptive PSO

2013 ◽  
Vol 14 (5) ◽  
pp. 487-498
Author(s):  
Rajendraprasad Narne ◽  
P.C. Panda
Keyword(s):  
Power System ◽  
Real Time ◽  
Optimization Problem ◽  
Test System ◽  
Swarm Optimization ◽  
Damping Controllers ◽  
Control Schemes ◽  
Controller Performance ◽  
Adaptive Pso ◽  
System Stabilizer

Abstract This article proposed coordinated tuning and real-time implementation of power system stabilizer (PSS) with static var compensator (SVC) in multi-machine power system. The design of proposed coordinated damping controller is formulated as an optimization problem, and the controller gains are optimized instantaneously using advanced adaptive particle swarm optimization. Here, PSS with SVC installed in multi-machine system is examined. The coordinated tuning among the damping controllers is performed on the non-linear power system dynamic model. Finally, the proposed coordinated controller performance is discussed with time-domain simulations. Different loading conditions are employed on the test system to test the robustness of proposed coordinate controller, and the simulation results are compared with four different control schemes. To validate the proposed controller, the test power system is also implemented on real-time (OPAL-RT) simulator, and acceptable results are reported for its verifications.

scholarly journals A Nonlinear System Identification Method Based on Adaptive Neural Network

2021 ◽  
Vol 28 (2) ◽  
pp. 111-123
Keyword(s):  
Neural Network ◽  
System Identification ◽  
Nonlinear System ◽  
Nonlinear System Identification ◽  
Generic Model ◽  
Control Engineering ◽  
The Neural Network ◽  
Adaptive Pso ◽  
And Control ◽  
Scientific Engineering

Nonlinear system identification (NSI) is of great significance to modern scientific engineering and control engineering. Despite their identification ability, the existing analysis methods for nonlinear systems have several limitations. The neural network (NN) can overcome some of these limitations in NSI, but fail to achieve desirable accuracy or training speed. This paper puts forward an NSI method based on adaptive NN, with the aim to further improve the convergence speed and accuracy of NN-based NSI. Specifically, a generic model-based nonlinear system identifier was constructed, which integrates the error feedback and correction of predictive control with the generic model theory. Next, the radial basis function (RBF) NN was optimized by adaptive particle swarm optimization (PSO), and used to build an NSI model. The effectiveness and speed of our model were verified through experiments. The research results provide a reference for applying the adaptive PSO-optimized RBFNN in other fields.

scholarly journals Parameter Identification of the Vortex-Induced Vertical Force Model Using a New Adaptive PSO

2018 ◽  
Vol 2018 ◽  
pp. 1-9
Author(s):  
Xiaoxia Tian ◽  
Jingwen Yan ◽  
Chi Xiao
Keyword(s):  
Optimal Solution ◽  
Time History ◽  
Vertical Displacement ◽  
Force Model ◽  
Vertical Force ◽  
Inertia Weight ◽  
Adaptive Pso ◽  
Short Run ◽  
History Of ◽  
Searching Ability

The paper proposes a new adaptive PSO (NAPSO) that adaptively adjust the inertial weight of every particle according to its own current fitness. In NAPSO, the searching ability of each particle is controlled by the inertial weight. In pursuit of the optimal solution, if a particle has a rather small value of normalized fitness, it has a small inertia weight so as to increase local searching ability; on the contrary, it has a large inertia weight to increase global searching ability. Simulation results include three parts: the NAPSO shows fast convergence and good stability compared with other PSOs; the NAPSO shows good fit and short run-time compared with GA and GALMA; according to the identified parameters, the time history of predicted vertical displacement is quite in accordance with the time history of measured displacement. As far as the nonlinear VIVF model is concerned, the NAPSO is a simple and effective identification method.

scholarly journals ADAI and Adaptive PSO-Based Resource Allocation for Wireless Sensor Networks

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 131163-131171 ◽  
Author(s):  
Amrit Mukherjee ◽  
Pratik Goswami ◽  
Ziwei Yan ◽  
Lixia Yang ◽  
Joel J. P. C. Rodrigues
Keyword(s):  
Resource Allocation ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Wireless Sensor ◽  
Adaptive Pso
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