Training ANFIS as an identifier with intelligent hybrid stable learning algorithm based on particle swarm optimization and extended Kalman filter

2009 ◽  
Vol 160 (7) ◽  
pp. 922-948 ◽  
Author(s):  
Mahdi Aliyari Shoorehdeli ◽  
Mohammad Teshnehlab ◽  
Ali Khaki Sedigh
Keyword(s):  
Particle Swarm Optimization ◽  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Learning Algorithm ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Stable Learning

scholarly journals An All-Region State-of-Charge Estimator Based on Global Particle Swarm Optimization and Improved Extended Kalman Filter for Lithium-Ion Batteries

Electronics ◽  
2018 ◽  
Vol 7 (11) ◽  
pp. 321 ◽  
Author(s):  
Xin Lai ◽  
Wei Yi ◽  
Yuejiu Zheng ◽  
Long Zhou
Keyword(s):  
Particle Swarm Optimization ◽  
Kalman Filter ◽  
Lithium Ion Batteries ◽  
Extended Kalman Filter ◽  
Particle Swarm ◽  
Lithium Ion ◽  
State Of Charge ◽  
Model Parameters ◽  
Swarm Optimization ◽  
Soc Estimation

In this paper, a novel model parameter identification method and a state-of-charge (SOC) estimator for lithium-ion batteries (LIBs) are proposed to improve the global accuracy of SOC estimation in the all SOC range (0–100%). Firstly, a subregion optimization method based on particle swarm optimization is developed to find the optimal model parameters of LIBs in each subregion, and the optimal number of subregions is investigated from the perspective of accuracy and computation time. Then, to solve the problem of a low accuracy of SOC estimation caused by large model error in the low SOC range, an improved extended Kalman filter (IEKF) algorithm with variable noise covariance is proposed. Finally, the effectiveness of the proposed methods are verified by experiments on two kinds of batteries under three working cycles, and case studies show that the proposed IEKF has better accuracy and robustness than the traditional extended Kalman filter (EKF) in the all SOC range.

scholarly journals A Novel KGP Algorithm for Improving INS/GPS Integrated Navigation Positioning Accuracy

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1623 ◽  
Author(s):  
Huibing Zhang ◽  
Tong Li ◽  
Lihua Yin ◽  
Dingke Liu ◽  
Ya Zhou ◽  
...  
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Kalman Filter ◽  
Decision Tree ◽  
Particle Swarm ◽  
Sensor Data ◽  
Gradient Boosting ◽  
Integrated Navigation ◽  
Swarm Optimization ◽  
Positioning Accuracy

The fusion of multi-source sensor data is an effective method for improving the accuracy of vehicle navigation. The generalization abilities of neural-network-based inertial devices and GPS integrated navigation systems weaken as the nonlinearity in the system increases, resulting in decreased positioning accuracy. Therefore, a KF-GDBT-PSO (Kalman Filter-Gradient Boosting Decision Tree-Particle Swarm Optimization, KGP) data fusion method was proposed in this work. This method establishes an Inertial Navigation System (INS) error compensation model by integrating Kalman Filter (KF) and Gradient Boosting Decision Tree (GBDT). To improve the prediction accuracy of the GBDT, we optimized the learning algorithm and the fitness parameter using Particle Swarm Optimization (PSO). When the GPS signal was stable, the KGP method was used to solve the nonlinearity issue between the vehicle feature and positioning data. When the GPS signal was unstable, the training model was used to correct the positioning error for the INS, thereby improving the positioning accuracy and continuity. The experimental results show that our method increased the positioning accuracy by 28.20–59.89% compared with the multi-layer perceptual neural network and random forest regression.

scholarly journals GENERALIZED CELLULAR NEURAL NETWORKS (GCNNs) CONSTRUCTED USING PARTICLE SWARM OPTIMIZATION FOR SPATIO-TEMPORAL EVOLUTIONARY PATTERN IDENTIFICATION

2008 ◽  
Vol 18 (12) ◽  
pp. 3611-3624 ◽  
Author(s):  
H. L. WEI ◽  
S. A. BILLINGS
Keyword(s):  
Neural Networks ◽  
Particle Swarm Optimization ◽  
Orthogonal Projection ◽  
Learning Algorithm ◽  
Projection Pursuit ◽  
Particle Swarm ◽  
Cellular Neural Networks ◽  
Modeling Framework ◽  
Swarm Optimization ◽  
Spatio Temporal

Particle swarm optimization (PSO) is introduced to implement a new constructive learning algorithm for training generalized cellular neural networks (GCNNs) for the identification of spatio-temporal evolutionary (STE) systems. The basic idea of the new PSO-based learning algorithm is to successively approximate the desired signal by progressively pursuing relevant orthogonal projections. This new algorithm will thus be referred to as the orthogonal projection pursuit (OPP) algorithm, which is in mechanism similar to the conventional projection pursuit approach. A novel two-stage hybrid training scheme is proposed for constructing a parsimonious GCNN model. In the first stage, the orthogonal projection pursuit algorithm is applied to adaptively and successively augment the network, where adjustable parameters of the associated units are optimized using a particle swarm optimizer. The resultant network model produced at the first stage may be redundant. In the second stage, a forward orthogonal regression (FOR) algorithm, aided by mutual information estimation, is applied to refine and improve the initially trained network. The effectiveness and performance of the proposed method is validated by applying the new modeling framework to a spatio-temporal evolutionary system identification problem.

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