Image Restoration by Using New AGA Optimized BPNN

Farooq, Umar; Shen, Tingzhi; Zhao, Sanyuan; Imran, Muhammad

doi:10.1016/j.proeng.2012.01.434

Cited by 4 publications

(2 citation statements)

References 7 publications

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“…where x t+m is the forecast value, x t is the actual value, and l denotes the lag orders. In ANN, the initial weights and thresholds have significant meaning and play an important role in learning and optimizing the neural network [40]. However, these parameters are randomly generated in the beginning and then adjusted in the whole training process.…”

Section: E Framework Of Our Proposed Approachmentioning

confidence: 99%

A New Decomposition Ensemble Learning Approach with Intelligent Optimization for PM_2.5 Concentration Forecasting

Xing

Sun

Guo

2020

Discrete Dynamics in Nature and Society

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In this study, we focus our attention on the forecasting of daily PM2.5 concentrations. According to the principle of “divide and conquer,” we propose a novel decomposition ensemble learning approach by integrating ensemble empirical mode decomposition (EEMD), artificial neural networks (ANNs), and adaptive particle swarm optimization (APSO) for forecasting PM2.5 concentrations. Our proposed decomposition ensemble learning approach is formulated exclusively to deal with difficulties in quantitating meteorological information with high volatility, irregularity, and complicacy. This decomposition ensemble learning approach mainly consists of three steps. First, we utilize EEMD to decompose original time series of PM2.5 concentrations into a specific amount of independent intrinsic mode functions (IMFs) and residual term. Second, the ANN, whose connection parameters are optimized by APSO algorithm, is employed to model IMFs and residual terms, respectively. Finally, another APSO-ANN is applied to aggregate the forecast IMFs and residual term into a collection as the final forecasting results. The empirical results show that the forecasting of our decomposition ensemble learning approach outperforms other benchmark models in terms of level accuracy and directional accuracy.

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Section: E Framework Of Our Proposed Approachmentioning

confidence: 99%

A New Decomposition Ensemble Learning Approach with Intelligent Optimization for PM_2.5 Concentration Forecasting

Xing

Sun

Guo

2020

Discrete Dynamics in Nature and Society

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show abstract

“…A GA is used to optimize a back propagation neural network in [45] applied to image restoration. The energy function for the reconstruction problem, formulated as a Bayesian framework, is minimized by a genetic algorithm in [46].…”

Section: Introductionmentioning

confidence: 99%

Image denoising based on genetic algorithm

Toledo

Oliveira

Silva

et al. 2013

2013 IEEE Congress on Evolutionary Computation

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Digital images play an essential role in analysis tasks with applications in various knowledge domains, such as medicine, meteorology, geology, biology, among others. Such images can be degraded by noise during the process of acquisition, transmission, storage or compression. Although several image denoising methods have been proposed in the literature, noise suppression in images still remains a challenging problem for researchers since the process can cause the removal of relevant image features, such as edges and corners. This papers describes a novel image denoising method based on a genetic algorithm. A population of noisy images is evolved for several epochs applying tailor-made crossover and mutation operators. The population is reinitialized every time a convergence occurs, when only the best individual (image) is kept for the next epoch. Experimental results demonstrate that the proposed method is competitive in comparison with state-of-the-art approaches.

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Analysis and forecasting of the particulate matter (PM) concentration levels over four major cities of China using hybrid models

Qin

Liu

Wang

et al. 2014

Atmospheric Environment

100

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Image Restoration by Using New AGA Optimized BPNN

Cited by 4 publications

References 7 publications

A New Decomposition Ensemble Learning Approach with Intelligent Optimization for PM_2.5 Concentration Forecasting

A New Decomposition Ensemble Learning Approach with Intelligent Optimization for PM_2.5 Concentration Forecasting

Image denoising based on genetic algorithm

Analysis and forecasting of the particulate matter (PM) concentration levels over four major cities of China using hybrid models

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Image Restoration by Using New AGA Optimized BPNN

Cited by 4 publications

References 7 publications

A New Decomposition Ensemble Learning Approach with Intelligent Optimization for PM2.5 Concentration Forecasting

A New Decomposition Ensemble Learning Approach with Intelligent Optimization for PM2.5 Concentration Forecasting

Image denoising based on genetic algorithm

Analysis and forecasting of the particulate matter (PM) concentration levels over four major cities of China using hybrid models

Contact Info

Product

Resources

About

A New Decomposition Ensemble Learning Approach with Intelligent Optimization for PM_2.5 Concentration Forecasting

A New Decomposition Ensemble Learning Approach with Intelligent Optimization for PM_2.5 Concentration Forecasting