An improved particle swarm optimization algorithm used for BP neural network and multimedia course-ware evaluation

Liu, Tianhua; Yin, Shoulin

doi:10.1007/s11042-016-3776-5

Cited by 77 publications

(48 citation statements)

References 9 publications

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“…Apart from the above works, BP neural network has also been used in other areas, such as Thermal error compensation of high-speed spindle system [11], Multimedia course-ware evaluation [10], Prediction of high-speed grinding temperature of titanium matrix [8], Sensor-less free space optics communication [7], Macroeconomic forecasting [6], Predicting of MODIS Leaf Area Index Time Series [5], Microclearance electrolysis-assisted laser machining [30], Identification and Adjustment of Guide Rail Geometric Errors [4], Prediction on the cutting process of constrained damping boring bars [3], Prediction of cut size for pneumatic classification [28], Temperature Sensing Research [23], and UGI Gasification Processes [9]. Then, the output of a neuron is transmitted to the inputs of all neurons of the next layer, and the output layer generates the final results of the ANN.…”

Section: Lidong Liu Fajie Wei and Shenghan Zhoumentioning

confidence: 99%

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Major project risk assessment method based on BP neural network

Liu¹,

Wei²,

Zhou³

2019

Discrete &Amp; Continuous Dynamical Systems - S

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In order to prevent risks in major projects, it is of great importance to accurately assess risks in advance. Therefore, in this paper, we propose a novel major project risk assessment method with the BP neural network model. Firstly, we propose an index system for major project risk assessment, which is made up of four parts: 1) Schedule risk, 2) Cost risk, 3) Quality risk, and 4) Resource risk. Secondly, we propose a hybrid BP neural network and particle swarm optimization (PSO) model to evaluate risks in major projects. Especially, major project risk assessment results are achieved from the output layers of the BP neural network which is optimized by the PSO algorithm. In our proposed hybrid model, the fitness for each particle is computed through an optimal function, and then the particle can improve its velocity for the next cycle by searching the optimal value. Furthermore, this process should be repeated when the end condition is satisfied. Finally, experimental results demonstrate that the proposed method is able to evaluate risk level of major projects with high accuracy.

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Section: Lidong Liu Fajie Wei and Shenghan Zhoumentioning

confidence: 99%

“…O j = F (net j ) = 1 + e −netj −1 (10) Afterwards, main differences between various hidden layers are estimated as follows.…”

Section: Lidong Liu Fajie Wei and Shenghan Zhoumentioning

confidence: 99%

Major project risk assessment method based on BP neural network

Liu¹,

Wei²,

Zhou³

2019

Discrete &Amp; Continuous Dynamical Systems - S

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“…With the fast growth of information science, the research of biological applications has been used for computational science to analyze the intelligent bionic optimization algorithm design and improve the ability of processing big data and analysis [3]. Intelligent bionic algorithms mainly include ant colony algorithm [4], particle swarm optimization (PSO) algorithm [5], and the quantum swarm algorithm [6][7][8]. Swarm intelligence optimization algorithms have a good application value in artificial intelligence design, data clustering analysis, computer control, and other fields.…”

Section: Introductionmentioning

confidence: 99%

Modified Immune Evolutionary Algorithm for Medical Data Clustering and Feature Extraction under Cloud Computing Environment

Liu

2020

Journal of Healthcare Engineering

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Medical data have the characteristics of particularity and complexity. Big data clustering plays a significant role in the area of medicine. The traditional clustering algorithms are easily falling into local extreme value. It will generate clustering deviation, and the clustering effect is poor. Therefore, we propose a new medical big data clustering algorithm based on the modified immune evolutionary method under cloud computing environment to overcome the above disadvantages in this paper. Firstly, we analyze the big data structure model under cloud computing environment. Secondly, we give the detailed modified immune evolutionary method to cluster medical data including encoding, constructing fitness function, and selecting genetic operators. Finally, the experiments show that this new approach can improve the accuracy of data classification, reduce the error rate, and improve the performance of data mining and feature extraction for medical data clustering.

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“…It is easy to cause the weight threshold calculation to fall into local minimum [15,16]. In order to improve the prediction accuracy, genetic algorithm (GA) [17] and particle swarm optimization (PSO) [18] are applied in the BP neural network to optimize the weight threshold in rockburst prediction.…”

Section: Introductionmentioning

confidence: 99%

Rockburst Prediction Model Based on Entropy Weight Integrated with Grey Relational BP Neural Network

Zheng

Zhong

Fang

et al. 2019

Advances in Civil Engineering

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A rockburst prediction model of the entropy weight grey relational backpropagation (BP) neural network is developed. The model needs to select the evaluation factors according to the engineering practice and establish the sample library. The entropy weight method is used to calculate the objective weight of the characteristic factors, and the similarity between the samples is calculated by the combination of grey relational theory and the entropy method. The training sample of the BP neural network is selected by threshold determination. Finally, we use the trained neural network to estimate the rockburst intensity grade of samples to be tested. This model is applied to the rockburst prediction of Qamchiq tunnel project, and the prediction results are in good agreement with the actual conditions of the subsequent construction, thus verifying the feasibility and effectiveness of the model in the rockburst prediction.

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An improved particle swarm optimization algorithm used for BP neural network and multimedia course-ware evaluation

Cited by 77 publications

References 9 publications

Major project risk assessment method based on BP neural network

Major project risk assessment method based on BP neural network

Modified Immune Evolutionary Algorithm for Medical Data Clustering and Feature Extraction under Cloud Computing Environment

Rockburst Prediction Model Based on Entropy Weight Integrated with Grey Relational BP Neural Network

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