A hybrid wavelet neural network model with mutual information and particle swarm optimization for forecasting monthly rainfall

He, Xinguang; Guan, Huade; Qin, Jianxin

doi:10.1016/j.jhydrol.2015.04.047

Cited by 88 publications

(26 citation statements)

References 45 publications

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“…where w max is the maximum value, w min is the minimum value, t is the current number of iterations, T max is the maximum number of iterations. The inertia weight is linearly declining [8]. Personal best X pb i (t + 1) of the particle i in the t + 1 iteration is updated with…”

Section: 1mentioning

confidence: 99%

“…(25) (7) The motion range of particles from crossing the boundaries of decision space is prevented according to the following ways: if the position of the particle exceeds the range of a decision variable, the position of the particle is the boundary value, and the velocity of the particle is set to opposite to that before. (8) The standard variance of the variation operator is calculated, and the multiscale variation operation is carried out for each particle position. (9) The mutated particles are recombined with the original population into a new population, and the non-inferior sets are updated and maintained on the basis of the objective vector of each particle.…”

Section: 1mentioning

confidence: 99%

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Multi-objective optimization algorithm based on improved particle swarm in cloud computing environment

Zhang¹,

Li²

2019

Discrete &Amp; Continuous Dynamical Systems - S

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In cloud computing environment, in order to optimize the deployment scheduling of resources, it is necessary to improve the accuracy of the optimal solution, guarantee the convergence ability of the algorithm, and improve the performance of cloud computing. In this paper, a multi-objective optimization algorithm based on improved particle swarm is proposed. A multiobjective optimization model is built. Improved multi-scale particle swarm is used to optimize the built multi-objective model. The combination of the global search capability and the local search capability of the algorithm is realized by using Gaussian variation operator with varied scales. The large scale Gaussian variation operator with concussion characteristics can complete fast global search for decision space, so that particles can quickly locate the surrounding area of the optimal solution, which enhances the ability to escape the local optimal solution of the algorithm and avoids the occurrence of precocious convergence. The small scale variation operator gradually reduces the area near the optimal solution. Experimental results show that the improved particle swarm optimization algorithm can effectively improve the precision of the optimal solution and ensure the convergence of the algorithm. 2010 Mathematics Subject Classification. 31C20.

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Section: 1mentioning

confidence: 99%

Section: 1mentioning

confidence: 99%

Multi-objective optimization algorithm based on improved particle swarm in cloud computing environment

Zhang¹,

Li²

2019

Discrete &Amp; Continuous Dynamical Systems - S

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“…Some particles in the particle swarm get a new target position by flight in the identification iteration. To obtain a new target position, some particles are randomly sampled following simulated annealing [16][17][18][19][20]. By calculating the acceptance probability of a new position, each particle can decide whether to reach a new position to ultimately update the position status of the entire particle swarm.…”

Section: Introductionmentioning

confidence: 99%

Application of Simulated Annealing Particle Swarm Optimization Based on Correlation in Parameter Identification of Induction Motor

Wang

Liu

2018

Mathematical Problems in Engineering

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The strengths and weaknesses of correlation algorithm, simulated annealing algorithm, and particle swarm optimization algorithm are studied in this paper. A hybrid optimization algorithm is proposed by drawing upon the three algorithms, and the specific application processes are given. To extract the current fundamental signal, the correlation algorithm is used. To identify the motor dynamic parameter, the filtered stator current signal is simulated using simulated annealing particle swarm algorithm. The simulated annealing particle swarm optimization algorithm effectively incorporates the global optimization ability of simulated annealing algorithm with the fast convergence of particle swarm optimization by comparing the identification results of asynchronous motor with constant torque load and step load.

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“…The utilization of the wavelet activation function also leads to faster learning ability, as compared to conventional multilayer perceptrons [3]. Even though the WNNs are continuously gaining prevalence [4,5], properly designing a WNN is a critical issue that needs to be addressed cautiously. Improperly modeling a WNN may jeopardize its generalization capability and predicting competence.…”

Section: Introductionmentioning

confidence: 99%

Calibrating wavelet neural networks by distance orientation similarity fuzzy C-means for approximation problems

Ong

Zainuddin

2016

Applied Soft Computing

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A hybrid wavelet neural network model with mutual information and particle swarm optimization for forecasting monthly rainfall

Cited by 88 publications

References 45 publications

Multi-objective optimization algorithm based on improved particle swarm in cloud computing environment

Multi-objective optimization algorithm based on improved particle swarm in cloud computing environment

Application of Simulated Annealing Particle Swarm Optimization Based on Correlation in Parameter Identification of Induction Motor

Calibrating wavelet neural networks by distance orientation similarity fuzzy C-means for approximation problems

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