Investigating the use of alternative topologies on performance of the PSO-ELM

Figueiredo, Elliackin; Ludermir, Teresa B.

doi:10.1016/j.neucom.2013.05.047

Cited by 56 publications

(21 citation statements)

References 17 publications

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“…Particle swarm optimization (PSO) is a iteration-based optimization algorithm based on the observation of the social behavior of biological organisms, such as birds in a flock. The particle swarm optimization algorithm uses the individuals sharing of information in the group to make the movement of the whole group to evolve from disorder to order in the problem solving space, so as to obtain the optimal solution [36].…”

Section: B Particle Swarm Optimizationmentioning

confidence: 99%

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PSO-ELM: A Hybrid Learning Model for Short-Term Traffic Flow Forecasting

Cai

Yang

et al. 2020

IEEE Access

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Accurate and reliable traffic flow forecasting is of importance for urban planning and mitigation of traffic congestion, and it is also the basis for the deployment of intelligent traffic management systems. However, constructing a reasonable and robust forecasting model is a challenging task due to the uncertainties and nonlinear characteristics of traffic flow. Aiming at the nonlinear relationship affecting traffic flow forecasting effect, a PSO-ELM model based on particle swarm optimization is proposed for short-term traffic flow forecasting, which takes the advantages of particle swarm optimization to search global optimal solution and extreme learning machine to fast deal with the nonlinear relationship. The proposed model improves the accuracy of traffic flow forecasting. The traffic flow data from highways A1, A2, A4, A8 connecting to Amsterdam's ring road are employed for the case study. The RMSEs of PSO-ELM model are respectively 252.61, 173.75, 200.24, 146.05, while the MAPEs of PSO-ELM model are respectively 11.86%, 10.10%, 10.74%, 11.60%. The experimental results show that the performance of the proposal is significantly better than the performance of state-of-the-art models. INDEX TERMS Short-term traffic flow forecasting, extreme learning machine, particle swarm optimization, time-series model.

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Section: B Particle Swarm Optimizationmentioning

confidence: 99%

“…Aiming at the above problems, this paper proposes an algorithm called PSO-ELM, which incorporates the particle swarm optimization and the extreme learning machine. In this model, the PSO algorithm optimizes the input weight matrix and the hidden layer bias in ELM to obtain an optimal network [36].…”

Section: Pso-elm For Traffic Flow Forecastingmentioning

confidence: 99%

PSO-ELM: A Hybrid Learning Model for Short-Term Traffic Flow Forecasting

Cai

Yang

et al. 2020

IEEE Access

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“…For each individual population, the range of each dimension is [−1, 1]. The hidden layer output matrix h i is calculated according to Equation (31), and the output weight β i is calculated according to Equation (33).…”

Section: Adaptive Control Algorithm Based On Strategy Self-adaptationmentioning

confidence: 99%

Research on Adaptive Reaction Null Space Planning and Control Strategy Based on VFF–RLS and SSADE–ELM Algorithm for Free-Floating Space Robot

Dong

Hong

2019

Electronics

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With the increase of on-orbit maintenance and support requirements, the application of a space manipulator is becoming more promising. In actual operation, the strong coupling of the free-floating space robot itself and the unknown disturbance of the contact target caused a major challenge to the robot base posture control. Traditional Reaction Null Space (RNS) motion planning and control methods require the construction of precise dynamic models, which is impossible in reality. In order to solve this problem, this paper proposes a new Adaptive Reaction Null Space (ARNS) path planning and control strategy for the contact of free-floating space robots with unknown targets. The ARNS path planning strategy is constructed by the Variable Forgetting Factor Recursive Least Squares (VFF–RLS) algorithm. At the same time, a robust adaptive control strategy based on the Strategy Self-Adaption Differential Evolution–Extreme Learning Machine (SSADE–ELM) algorithm is proposed to track the dynamic changes of the planned path. The algorithm enables us to intelligently learn and compensate for the unknown disturbance. Then, this paper constructs a robust controller to compensate model uncertainty. A striking feature of the proposed strategy is that it does not require an accurate system model or any information about unknown attributes. This design can dynamically implement RNS path tracking performance. Finally, through simulation and experiment, the proposed algorithm is compared with the existing methods to prove its effectiveness and superiority.

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“…In order to solve this problem, an improved ELM method based on particle swarm optimization (PSO) is proposed in [23]. This method can resolve the drawbacks of ELM.…”

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

Radar Target Classification Using an Evolutionary Extreme Learning Machine Based on Improved Quantum‐Behaved Particle Swarm Optimization

Zhao¹,

Liu²,

Huang³

et al. 2017

Mathematical Problems in Engineering

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A novel evolutionary extreme learning machine (ELM) based on improved quantum-behaved particle swarm optimization (IQPSO) for radar target classification is presented in this paper. Quantum-behaved particle swarm optimization (QPSO) has been used in ELM to solve the problem that ELM needs more hidden nodes than conventional tuning-based learning algorithms due to the random set of input weights and hidden biases. But the method for calculating the characteristic length of Delta potential well of QPSO may reduce the global search ability of the algorithm. To solve this issue, a new method to calculate the characteristic length of Delta potential well is proposed in this paper. Experimental results based on the benchmark functions validate the better performance of IQPSO against QPSO in most cases. The novel algorithm is also evaluated by using real-world datasets and radar data; the experimental results indicate that the proposed algorithm is more effective than BP, SVM, ELM, QPSO-ELM, and so on, in terms of real-time performance and accuracy.

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Investigating the use of alternative topologies on performance of the PSO-ELM

Cited by 56 publications

References 17 publications

PSO-ELM: A Hybrid Learning Model for Short-Term Traffic Flow Forecasting

PSO-ELM: A Hybrid Learning Model for Short-Term Traffic Flow Forecasting

Research on Adaptive Reaction Null Space Planning and Control Strategy Based on VFF–RLS and SSADE–ELM Algorithm for Free-Floating Space Robot

Radar Target Classification Using an Evolutionary Extreme Learning Machine Based on Improved Quantum‐Behaved Particle Swarm Optimization

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