An incremental learning algorithm for the hybrid RBF-BP network classifier

Wen, Hui; Xie, Weixin; Pei, Jihong; Guan, Lixin

doi:10.1186/s13634-016-0357-8

Cited by 16 publications

(9 citation statements)

References 30 publications

(22 reference statements)

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“…RBF neural networks can compensate for external environmental disturbances. A PD + RBF control algorithm is combined, which improves the immunity and robust of the power positioning system (Wen et al, 2016;Huang et al, 2021). An adaptive fuzzy SMC algorithm is proposed to the positioning control problem of articulated robots, and the steady-state convergence is good and has some robustness (Zirkohi and Fateh, 2017).…”

Section: Related Workmentioning

confidence: 99%

Self-Tuning Control of Manipulator Positioning Based on Fuzzy PID and PSO Algorithm

Liu

Yun

et al. 2022

Front. Bioeng. Biotechnol.

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With the manipulator performs fixed-point tasks, it becomes adversely affected by external disturbances, parameter variations, and random noise. Therefore, it is essential to improve the robust and accuracy of the controller. In this article, a self-tuning particle swarm optimization (PSO) fuzzy PID positioning controller is designed based on fuzzy PID control. The quantization and scaling factors in the fuzzy PID algorithm are optimized by PSO in order to achieve high robustness and high accuracy of the manipulator. First of all, a mathematical model of the manipulator is developed, and the manipulator positioning controller is designed. A PD control strategy with compensation for gravity is used for the positioning control system. Then, the PID controller parameters dynamically are minute-tuned by the fuzzy controller 1. Through a closed-loop control loop to adjust the magnitude of the quantization factors–proportionality factors online. Correction values are outputted by the modified fuzzy controller 2. A quantization factor–proportion factor online self-tuning strategy is achieved to find the optimal parameters for the controller. Finally, the control performance of the improved controller is verified by the simulation environment. The results show that the transient response speed, tracking accuracy, and follower characteristics of the system are significantly improved.

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Section: Related Workmentioning

confidence: 99%

Self-Tuning Control of Manipulator Positioning Based on Fuzzy PID and PSO Algorithm

Liu

Yun

et al. 2022

Front. Bioeng. Biotechnol.

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“…Through the given data, by using the self-learning function of back propagation(BP) neural network to determine whether users match. BP neural network has been widely used by scholars because of its strong learning ability, flexible modeling ability and large-scale parallel computing ability, and is widely used in image processing [26], economic forecasting [27], network classifier [28] and other fields. The traditional BP neural network adjusts the weights and thresholds by using the gradient descent method.…”

Section: )Application Analysis Of Otafpa Algorithm In User Identification Cross Social Networkmentioning

confidence: 99%

Improved Flower Pollination Algorithm and Its Application in User Identification Across Social Networks

Jian

et al. 2019

IEEE Access

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Aiming at the shortages of basic flower pollination algorithm (FPA) with slow convergence speed, low search precision, and easy to fall into local optimum, a new adaptive FPA based on opposition-based learning and t-distribution (OTAFPA) was proposed and be applied to the social networks. First, the opposition-based learning strategy is utilized to increase the diversity and quality of the initial population. Then, the adaptive dynamic switching probability is introduced, which can effectively balance the global and local search according to the current number of iterations. Finally, the t-distribution variation is used to increase the population diversity and to help the algorithm jump out of the local optimum. The simulation experiments on eight classical test functions show that OTAFPA has better global optimization ability, which improves the convergence speed and the solution accuracy of the algorithm. The OTAFPA also shows superior performance in practical applications of user identification across social networks.

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“…To generate the optimal number and parameters of the RBF kernel, in our previous work, an incremental learning algorithm for the hybrid RBF-BP network (ILRBF-BP) [22] and a hybrid structure adaptive RBF-ELM network (HSARBF-ELM) [23] are presented. In ILRBF-BP, the method of potential density clustering is presented to generate RBF kernels automatically, which utilizes the global distribution information of sample space.…”

Section: Introductionmentioning

confidence: 99%

An Optimized Neural Network Classification Method Based on Kernel Holistic Learning and Division

Wen

Chen

et al. 2021

Mathematical Problems in Engineering

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An optimized neural network classification method based on kernel holistic learning and division (KHLD) is presented. The proposed method is based on the learned radial basis function (RBF) kernel as the research object. The kernel proposed here can be considered a subspace region consisting of the same pattern category in the training sample space. By extending the region of the sample space of the original instances, relevant information between instances can be obtained from the subspace, and the classifier’s boundary can be far from the original instances; thus, the robustness and generalization performance of the classifier are enhanced. In concrete implementation, a new pattern vector is generated within each RBF kernel according to the instance optimization and screening method to characterize KHLD. Experiments on artificial datasets and several UCI benchmark datasets show the effectiveness of our method.

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An incremental learning algorithm for the hybrid RBF-BP network classifier

Cited by 16 publications

References 30 publications

Self-Tuning Control of Manipulator Positioning Based on Fuzzy PID and PSO Algorithm

Self-Tuning Control of Manipulator Positioning Based on Fuzzy PID and PSO Algorithm

Improved Flower Pollination Algorithm and Its Application in User Identification Across Social Networks

An Optimized Neural Network Classification Method Based on Kernel Holistic Learning and Division

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