Self-adaptive extreme learning machine

Wang, Gai-Ge; Mei, Lu; Dong, Yongquan; Zhao, Xiangjun

doi:10.1007/s00521-015-1874-3

Cited by 126 publications

(50 citation statements)

References 52 publications

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“…Despite the significant number of recent publications in this field [21][22][23][24][25][26][27][28][29], there are still other swarming behaviours in nature that have not gained deserved attention. One of the fancy insects that rarely swarm are dragonflies.…”

Section: Introductionmentioning

confidence: 93%

Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems

Mirjalili

2015

Neural Comput & Applic

2,276

1,010

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A novel swarm intelligence optimization technique is proposed called dragonfly algorithm (DA). The main inspiration of the DA algorithm originates from the static and dynamic swarming behaviours of dragonflies in nature. Two essential phases of optimization, exploration and exploitation, are designed by modelling the social interaction of dragonflies in navigating, searching for foods, and avoiding enemies when swarming dynamically or statistically. The paper also considers the proposal of binary and multi-objective versions of DA called binary DA (BDA) and multi-objective DA (MODA), respectively. The proposed algorithms are benchmarked by several mathematical test functions and one real case study qualitatively and quantitatively. The results of DA and BDA prove that the proposed algorithms are able to improve the initial random population for a given problem, converge towards the global optimum, and provide very competitive results compared to other well-known algorithms in the literature. The results of MODA also show that this algorithm tends to find very accurate approximations of Pareto optimal solutions with high uniform distribution for multi-objective problems. The set of designs obtained for the submarine propeller design problem demonstrate the merits of MODA in solving challenging real problems with unknown true Pareto optimal front as well. Note that the source codes of the DA, BDA, and MODA algorithms are publicly available at

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Section: Introductionmentioning

confidence: 93%

Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems

Mirjalili

2015

Neural Comput & Applic

2,276

1,010

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“…) in two sequential stages: random feature projection and linear parameter solving [17,24,35,40,45]. In the first ELM stage, the hidden layer parameters (fw i ; b i g n h i¼1 ) are randomly initialized to project the input data to a random ELM feature space using the mapping function g().…”

Section: Extreme Learning Machinesmentioning

confidence: 99%

Representation learning with deep extreme learning machines for efficient image set classification

Uzair

Ghanem

Mian

2016

Neural Comput & Applic

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Efficient and accurate representation of a collection of images, that belong to the same class, is a major research challenge for practical image set classification. Existing methods either make prior assumptions about the data structure, or perform heavy computations to learn structure from the data itself. In this paper, we propose an efficient image set representation that does not make any prior assumptions about the structure of the underlying data. We learn the nonlinear structure of image sets with deep extreme learning machines that are very efficient and generalize well even on a limited number of training samples. Extensive experiments on a broad range of public datasets for image set classification show that the proposed algorithm consistently outperforms state-of-the-art image set classification methods both in terms of speed and accuracy.

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“…In 2006, Huang et al put forward the extreme learning concepts of feedforward neural networks and introduce the basic principle in detail. Extreme Learning Machine (ELM) [25] is a special type of single hidden layer feedforward neural network (SLFN) with only one hidden node layer [26]. It was later extended to general SLFN, and its hidden node is similar to…”

Section: Output Nodementioning

confidence: 99%

The Action Control Model for Robotic Fish Using Improved Extreme Learning Machine

et al. 2019

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To achieve fast and accurate adjustment of robotic fish, this paper proposes state prediction model based on the extreme learning machine optimized by particle swarm algorithm. The proposed model can select desirable actions for robotic fish according to precisely predicted states, "adjusting position" or "pushing ball" defined herein. Specifically, the extreme learning machine (ELM) is leveraged to predict the state of robotic fish, from the observations of current surrounding environment. As the outputs in ELM are varying with the randomly initialized parameters, particle swarm optimization (PSO) algorithm further improves the accuracy and robustness of the ELM by optimizing initial parameters. The empirical results on URWPGSim2D simulation platform indicate that the robotic fish tends to carry out appropriate actions using the state prediction model so that we can complete the game efficiently. It proves that the proposed model can make best use of the real-time information of robotic fish and water polo and derive fulfilling action strategy in various scenarios, which meet the requirements of motion control for robotic fish.

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Self-adaptive extreme learning machine

Cited by 126 publications

References 52 publications

Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems

Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems

Representation learning with deep extreme learning machines for efficient image set classification

The Action Control Model for Robotic Fish Using Improved Extreme Learning Machine

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