Novel adaptive hybrid rule network based on TS fuzzy rules using an improved quantum-behaved particle swarm optimization

Lin, Lin; Guo, Feng; Xie, Xu; Luo, Bin

doi:10.1016/j.neucom.2014.07.033

Cited by 31 publications

(12 citation statements)

References 39 publications

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“…In order to further prove the optimization performance of the proposed IPSO in training the FNN model, the basic PSO [26], quantum-behaved PSO (QPSO) [27], opinion leader-based QPSO (OLB-QPSO) [28], and Laplace PSO (LPSO) [29] were employed to optimize the parameters in the FNN model. The parameters were set according to the relevant literatures.…”

Section: Experiments Resultsmentioning

confidence: 99%

Health Condition Evaluation for a Shearer through the Integration of a Fuzzy Neural Network and Improved Particle Swarm Optimization Algorithm

Wang

Liu

et al. 2016

Applied Sciences

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Abstract:In order to accurately evaluate the health condition of a shearer, a hybrid prediction method was proposed based on the integration of a fuzzy neural network (FNN) and improved particle swarm optimization (IPSO). The parameters of FNN were optimized by the use of PSO, which was coupled with a premature judgment and mutation mechanism to increase the convergence speed and enhance the generalization ability. The key technologies are elaborated and the flowchart of the proposed approach was designed. Furthermore, an experiment example was carried out and the comparison results indicated that the proposed approach was feasible and outperforms others. Finally, a field application example in coal mining face was demonstrated to specify the effect of the proposed system.

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Section: Experiments Resultsmentioning

confidence: 99%

Health Condition Evaluation for a Shearer through the Integration of a Fuzzy Neural Network and Improved Particle Swarm Optimization Algorithm

Wang

Liu

et al. 2016

Applied Sciences

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“…Using wave function ψ to determine the state of particles in quantum space, the probability of a particle appearing at a certain position in space can be expressed by |ψ| 2 . If the potential well in D dimension is pb id (t) in the t-th iteration of particle i [13][14][15].…”

Section: Qpso Algorithm Modelmentioning

confidence: 99%

A QPSO algorithm based on hierarchical weight and its application in cloud computing task scheduling

et al. 2021

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The computing method of the average optimal position is one of the most important factors that affect the optimization performance of the QPSO algorithm. Therefore, a particle position weight computing method based on particle fitness value grading is proposed, which is called HWQPSO (hierarchical weight QPSO). In this method, the higher the fitness value of a particle, the higher the level of the particle, and the greater the weight. Particles at different levels have different weights, while particles at the same level have the same weight. Through this method, the excellent particles have higher average optimal position weight, and at the same time, the absolute weight of a few particles is avoided, so that the algorithm can quickly and stably converge to the optimal solution, and improve the optimization ability and efficiency of the algorithm. In order to verify the effective ness of the method, five standard test functions are selected to test the performance of HWQPSO, QPSO, DWC-QPSO and LTQPSO algorithm, and the algorithms are applied to the task scheduling of the cloud computing platform. Through the test experiment and application comparison, the results show that the HWQPSO algorithm can converge to the optimal solution of the test function faster than the other three algorithms. It can also find the task scheduling scheme with the shortest time consumption and the most balanced computing resource load in the cloud platform. In the experiment, compared with QPSO, DWC-QPSO and LTQPSO algorithm, HWQPSO execution time of the maximum task scheduling was reduced by 35%, 23% and 21% respectively.

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“…The main disadvantage of PSO is that it is easily to trap into local optima, even if it converges in a very fast way [30]. Inspired by trajectory analysis and quantum mechanics, the quantum-behaved PSO (QPSO) is developed.…”

Section: Qpsomentioning

confidence: 99%

Preliminary research on abnormal brain detection by wavelet-energy and quantum- behaved PSO

Zhang

Yang

et al. 2016

THC

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Abstract. It is important to detect abnormal brains accurately and early. The wavelet-energy (WE) was a successful feature descriptor that achieved excellent performance in various applications; hence, we proposed a WE based new approach for automated abnormal detection, and reported its preliminary results in this study. The kernel support vector machine (KSVM) was used as the classifier, and quantum-behaved particle swarm optimization (QPSO) was introduced to optimize the weights of the SVM. The results based on a 5 × 5-fold cross validation showed the performance of the proposed WE + QPSO-KSVM was superior to "DWT + PCA + BP-NN", "DWT + PCA + RBF-NN", "DWT + PCA + PSO-KSVM", "WE + BPNN", "WE + KSVM", and "DWT + PCA + GA-KSVM" w.r.t. sensitivity, specificity, and accuracy. The work provides a novel means to detect abnormal brains with excellent performance.

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Novel adaptive hybrid rule network based on TS fuzzy rules using an improved quantum-behaved particle swarm optimization

Cited by 31 publications

References 39 publications

Health Condition Evaluation for a Shearer through the Integration of a Fuzzy Neural Network and Improved Particle Swarm Optimization Algorithm

Health Condition Evaluation for a Shearer through the Integration of a Fuzzy Neural Network and Improved Particle Swarm Optimization Algorithm

A QPSO algorithm based on hierarchical weight and its application in cloud computing task scheduling

Preliminary research on abnormal brain detection by wavelet-energy and quantum- behaved PSO

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