Sliding mode control based on a hybrid grey-wolf-optimized extreme learning machine for robot manipulators

Zhou, Zhiyu; Wang, Chao; Zhu, Zefei; Wang, Yaming; Yang, Donghe

doi:10.1016/j.ijleo.2019.01.105

Cited by 31 publications

(20 citation statements)

References 35 publications

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“…In order to satisfy the first requirement, a deviation to the whole and a stretching on the amplitude can, respectively, apply to the Gaussian distribution; at the same time, using the "three-sigma principle" of the Gaussian distribution, let the three times of the standard deviation equal to the radius of the niche to meet the second requirement. us, equation (9) can be listed and substituted into equation (8) to calculate the coefficient K and the standard deviation sigma, as shown in equation (10). e fitness function based on the Gaussian distribution function after modification is shown in equation (11), and its comparison with the classical fitness function is shown in Figure 1…”

Section: Modification To Fitness Sharing Functionmentioning

confidence: 99%

“…Zhang et al [7] combined whale optimization algorithm (WOA) with GA by adding WOA operations to solve the dynamic modeling problem of manipulators. A similar method was also proposed by Zhou et al [8], in which grey wolf optimization (GWO) was utilized, and grey wolf hunting behaviors were introduced before GA's operations. e proposed hybrid GA with GWO can provide input weights and biases of an extreme learning machine.…”

Section: Introductionmentioning

confidence: 99%

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A Dual-Thread Method for Time-Optimal Trajectory Planning in Joint Space Based on Improved NGA

Zhang

Liu

Wang

2020

Journal of Robotics

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To solve the problem that the time-consuming optimization process of Genetic Algorithm (GA) can erode the expected time-saving brought by the algorithm, time-optimal trajectory planning based on cubic spline was used, after the modification to classical fitness sharing function of NGA, a dual-threaded method utilizing elite strategy characteristic was designed which was based on Niche Genetic Algorithm (NGA) with the fitness sharing technique. The simulation results show that the proposed method can mitigate the contradiction of the long term the optimization algorithm takes but a short running time the trajectory gets, demonstrating the effectiveness of the proposed method. Besides, the improved fitness sharing technique has reduced the subjective process of determining relevant parameters and the optimized trajectory results met performance constraints of the robot joints.

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Section: Modification To Fitness Sharing Functionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Dual-Thread Method for Time-Optimal Trajectory Planning in Joint Space Based on Improved NGA

Zhang

Liu

Wang

2020

Journal of Robotics

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“…To enhance the performance of the construction of network, the ELM neural networks of the connection between the hidden layers and the output layers does not need to be iterative [18]- [21]. The characteristics of the algorithm are reflected in the selection process of the neural network parameters.…”

Section: Two-layer Extreme Learning Machine a Extreme Learning Mmentioning

confidence: 99%

Research on a Method of Gross Error Elimination for Slope Monitoring Data Based on Machine Learning

Xiao

et al. 2019

IEEE Access

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Many high and steep slopes are formed in the open pit mining process, whose stability will directly affect the construction and safety. Therefore, it is particularly important to detect and eliminate rough errors of slope monitoring data. This paper proposes an improved two-layer extreme learning machine (TELM) based on particle swarm optimization (PSO) to establish monitoring slope model. In particular, the parameters of the first layer of the whole algorithm are obtained by particle swarm optimization algorithm, and the parameters of the second layer are calculated through a new solution method. First, some basic principles and concepts of extreme learning machine are introduced, and the algorithm thought, basic principles and related derivation of the improved two-layer extreme learning machine in this paper are emphatically discussed. A better mapping relationship between the input space and the output space is found through the parameter solution method, which improves the precision of the algorithm. Then, the data collection and traditional gross error elimination methods are studied. Finally, in order to test the ability of the two hidden layer extreme learning machine to solve the slope monitoring problem, the algorithm is applied to the gross error elimination of the slope data. The experimental results show that compared with the traditional gross error elimination method and ELM algorithm, the proposed method has higher precision, stronger robustness, and more practicability. INDEX TERMS Extreme learning machine (ELM), gross error elimination, two-layer extreme learning machine, particle swarm optimization.

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“…Therefore, some scholars apply swarm intelligence optimization algorithm to the selection of penalty coefficient and kernel width of ELM. E. Sevinç [34] proposed a novel evolutionary feature selection algorithm integrated with ELM and provided significant improvements on classification accuracy, Zhiyu Zhou et al [35] proposed a the scheme of sliding mode control (SMC) based on extreme learning machine (ELM) in order to improve the control accuracy of a robot manipulator. Lyu et al [36] applied the improved bacterial foraging optimization (IBFO) algorithm to the selection of ELM super parameters, and made a successful application in the diagnosis of somatization disorders.…”

Section: Introductionmentioning

confidence: 99%

Detection of Peach Disease Image Based on Asymptotic Non-Local Means and PCNN-IPELM

Huang

Zhou

et al. 2020

IEEE Access

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Aiming at the problems of noise, background interference and low detection in peach disease image, this paper proposes a detection method of peach disease based on the asymptotic non-local means (ANLM) image algorithm and the fusion of parallel convolution neural network (PCNN) and extreme learning machine(ELM) optimized by linear particle swarm optimization(IPSO). Firstly, the method uses the ANLM image denoising algorithm to reduce the interference of the complex background in the image, then uses the parallel convolution neural network proposed by this paper to identify the characteristics of peach disease, uses the improved elu activation function instead of the conventional ReLu activation function, and uses the linear particle swarm optimized ELM (IPELM) proposed by this paper in the last layer instead of the traditional softmax layer, the update method is improved from two aspects to improve the convergence speed and accuracy of the network effectively. The results of 25513 images showed that the highest detection accuracy of brown rot, black spot, anthracnose, scab and normal peach were 89.02, 90.56, 85.37, 86.70 and 89.91 percent respectively, which indicated that this method was an effective method for peach disease detection.

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Sliding mode control based on a hybrid grey-wolf-optimized extreme learning machine for robot manipulators

Cited by 31 publications

References 35 publications

A Dual-Thread Method for Time-Optimal Trajectory Planning in Joint Space Based on Improved NGA

A Dual-Thread Method for Time-Optimal Trajectory Planning in Joint Space Based on Improved NGA

Research on a Method of Gross Error Elimination for Slope Monitoring Data Based on Machine Learning

Detection of Peach Disease Image Based on Asymptotic Non-Local Means and PCNN-IPELM

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