A Hybrid PSO-Fuzzy Model for Determining the Category of 85th Speed

Mahmoudabadi, Abbas; Ghazizadeh, Ali

doi:10.1155/2013/964262

Cited by 4 publications

(6 citation statements)

References 23 publications

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“…The introduction of w p significantly improves the performance of the PSO algorithm such as adjusting the search ability of particles in global and local. Also, the introduction of w p offsets the problem of the standard PSO algorithm and makes it apply to more practical problems [31]. Figure 6 illustrates the overall view of particle swarm optimization.…”

Section: Particle Swarm Optimization Algorithm (Pso)mentioning

confidence: 99%

“…The chaos has been a kind of novel global optimization technique. People pay much attention to the research of the Journal of Sensors optimization method based on the chaotic search [29,31,36,38]. At present, some scholars have applied it to the optimization of neural network weights and achieved good results.…”

Section: Offline Tuning Based On the Cpsomentioning

confidence: 99%

“…The chaotic motion is very sensitive to the initial value selection, and the different initial values will be different. The basic principle of the CPSO algorithm is that chaos initialization is adopted to improve individual quality and chaos perturbation is utilized to avoid the search being trapped in local optimum [31]. The process of the CPSO is conducted as follows:…”

Section: Offline Tuning Based On the Cpsomentioning

confidence: 99%

“…Once getting trapped in local optimum during the process of optimization, it is very easy to cause all particles to stagnate in the extreme value point [30]. Therefore, when the algorithm runs into prematurity, the random perturbation strategy is adopted for the best individual and the randomly selected individual to help them being out of the local minimum [31]. Since the PSO has the drawback of stopping optimizing when reaching a near-optimal solution [27], the chaos mechanism is proposed to help the PSO to optimize the searching result.…”

Section: Introductionmentioning

confidence: 99%

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Parameter Optimization on FNN/PID Compound Controller for a Three-Axis Inertially Stabilized Platform for Aerial Remote Sensing Applications

et al. 2019

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This paper presents a composite parameter optimization method based on the chaos particle swarm optimization and the back propagation algorithms for a fuzzy neural network/proportion integration differentiation compound controller, which is applied for an aerial inertially stabilized platform for aerial remote sensing applications. Firstly, a compound controller combining both the adaptive fuzzy neural network and traditional PID control methods is developed to deal with the contradiction between the control precision and robustness due to disturbances. Then, on the basis of both the chaos particle swarm optimization and the back propagation compound algorithms, the parameters of the fuzzy neural network/PID compound controller are optimized offline and fine-tuned online, respectively. In this way, the compound controller can achieve good adaptive convergence so as to get high stabilization precision under the multisource dynamic disturbance environment. To verify the method, the simulations are carried out. The results show that the composite parameter optimization method can effectively enhance the convergence of the controller, by which the stabilization precision and disturbance rejection capability of the proposed fuzzy neural network/PID compound controller are improved obviously.

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Section: Particle Swarm Optimization Algorithm (Pso)mentioning

confidence: 99%

Section: Offline Tuning Based On the Cpsomentioning

confidence: 99%

Section: Offline Tuning Based On the Cpsomentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Parameter Optimization on FNN/PID Compound Controller for a Three-Axis Inertially Stabilized Platform for Aerial Remote Sensing Applications

et al. 2019

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“…Particle swarm optimization algorithm (PSO) is a new optimization algorithm by the simulation of the flock's foraging behavior [9]. It can reach the optimal location by the cooperation of the whole group.…”

Section: Particle Swarm Optimization Algorithmmentioning

confidence: 99%

Study on Fuzzy Energy Management Strategy of Parallel Hybrid Vehicle Based on Quantum PSO Algorithm

Lihao¹,

Wang²,

Congmin³

2016

IJMUE

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A fuzzy energy management strategy was designed for the single-axle parallel hybrid electric vehicles, and then the quantitative factor of the fuzzy logic controller was optimized by the quantum PSO algorithm under the Matlab platform based on the equivalent fuel economy. Then a comparison test about the energy management before and after the optimization was carried out based on the secondary development of Advisor which proves that the energy management strategy optimized by the quantum PSO algorithm can improve the SOC of the battery pack by 18% when the VAIL2NREL cycle finished while the power of the vehicles nearly remain the same. What's more, the optimized strategy can make the engine and the motor works in the high efficient area for most of the time which can improve the recycling rate of the energy and reduce the equivalent fuel consumption effectively.

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Short-term origin–destination flow prediction for urban rail network: a deep learning method based on multi-source big data

Cui,

Si,

Wang

et al. 2024

Complex Intell. Syst.

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Short-term prediction of origin–destination (OD) flow is a primary but complex assignment to urban rail companies, which is the basis of intelligent and real-time urban rail transit (URT) operation and management. The short-term prediction of URT OD flow has three special characteristics: data lag, data dimensionality, and data malconformation, distinguishing it from other short-term prediction tasks. It is essential to propose a novel prediction algorithm that considers the special characteristics of the URT OD flow. For this purpose, based on deep learning methods and multi-source big data, a modified spatial–temporal long short-term memory (ST-LSTM) model is established. The proposed model comprises four components: (1) a temporal feature extraction module is devised to extract time information within network-wide historical OD data; (2) a spatial correlation learning module is introduced to address the data malconformation and data dimensionality problems, which provides an interpretable spatial correlation quantization method; (3) an input control-gated mechanism is originally proposed to solve the data lag problem, which combines the processed available OD flow and real-time inflow/outflow; (4) a fusion module combines historical spatial–temporal features with real-time information to achieve accurate OD flow prediction. We also further discuss the interpretability of the model in detail. The ST-LSTM model is evaluated by sufficient experiments on two large-scale actual subway datasets from Nanjing and Beijing, and the experimental results demonstrate that it can better learn the spatial–temporal correlations and exceed the rest benchmarking methods.

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A Hybrid PSO-Fuzzy Model for Determining the Category of 85th Speed

Cited by 4 publications

References 23 publications

Parameter Optimization on FNN/PID Compound Controller for a Three-Axis Inertially Stabilized Platform for Aerial Remote Sensing Applications

Parameter Optimization on FNN/PID Compound Controller for a Three-Axis Inertially Stabilized Platform for Aerial Remote Sensing Applications

Study on Fuzzy Energy Management Strategy of Parallel Hybrid Vehicle Based on Quantum PSO Algorithm

Short-term origin–destination flow prediction for urban rail network: a deep learning method based on multi-source big data

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