Convergence analysis of BP neural networks via sparse response regularization

Wang, Jian; Wen, Yanqing; Ye, Zhenyun; Jian, Ling; Chen, Hua

doi:10.1016/j.asoc.2017.07.059

Cited by 45 publications

(9 citation statements)

References 24 publications

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“…The ACO is a positive feedback mechanism. Therefore, based on this background, this paper uses ACO to train the parameters and structure of the feed-forward neural network and offers certain application value [13], [14].…”

Section: Related Workmentioning

confidence: 99%

A Novel Heuristic Artificial Neural Network Model for Urban Computing

Yin

Liu

2019

IEEE Access

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Urban computing brings powerful computational techniques to bear on such urban challenges as pollution, energy consumption, and traffic congestion. After decades of rapid development, artificial neural networks (ANN) have been successfully applied in many disciplines and have enabled many remarkable research achievements. However, no quantitative method has yet been found that can identify every parameter to optimize a neural network. The BP neural network is most frequently used but suffers from the following defects with respect to complex and multidimensional training data or setting of different parameters, i.e., overfitting, slow convergence speed, trapping in local optima and poor prediction effect, and these obstacles have greatly restricted its practical applications. Therefore, this paper proposes a method that uses ant colony optimization (ACO) to train the parameters and structure of the neural network, optimizes its weight and threshold to solve its defects, and applies the model in the optimization scheme of urban operation and management to verify its effect. The experimental simulation proves that the method in this paper is effective and that it makes certain improvements in the local and global search ability, speed, and accuracy of the neural network.INDEX TERMS Urban computing, artificial neural network model, structure and parameter, ant colony optimization, speed and accuracy.

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Section: Related Workmentioning

confidence: 99%

A Novel Heuristic Artificial Neural Network Model for Urban Computing

Yin

Liu

2019

IEEE Access

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“…Aimed at BP neural network can lead to poor generalization and slow convergence. The author has developed a variety of sparse response BP algorithm, effectively improve the generalization performance as stated in [21]. Some scholar introduced a new model developed by the Real Estate Valuation Center at the Polytechnic of Milan and validated the model over artificial neural networks using real estate data from Italy as stated in [22].…”

Section: Artificial Neural Network Model Evolution and Algorithm Improvementmentioning

confidence: 99%

Artificial Neural Networks and the Mass Appraisal of Real Estate

Zhou

Chen

et al. 2018

Int. J. Onl. Eng.

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<p>With the rapid development of computer, artificial intelligence and big data technology, artificial neural networks have become one of the most powerful machine learning algorithms. In the practice, most of the applications of artificial neural networks use back propagation neural network and its variation. Besides the back propagation neural network, various neural networks have been developing in order to improve the performance of standard models. Though neural networks are well known method in the research of real estate, there is enormous space for future research in order to enhance their function. Some scholars combine genetic algorithm, geospatial information, support vector machine model, particle swarm optimization with artificial neural networks to appraise the real estate, which is helpful for the existing appraisal technology. The mass appraisal of real estate in this paper includes the real estate valuation in the transaction and the tax base valuation in the real estate holding. In this study we focus on the theoretical development of artificial neural networks and mass appraisal of real estate, artificial neural networks model evolution and algorithm improvement, artificial neural networks practice and application, and review the existing literature about artificial neural networks and mass appraisal of real estate. Finally, we provide some suggestions for the mass appraisal of China's real estate.</p>

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“…On the basis of avoiding the overfitting problem of neural network algorithm and ensuring its generalization ability, the efficiency of neural network algorithm becomes the focus of optimization algorithm and its application in spectral analysis . In contrast, BP neural networks are generally considered to have advantages in improving algorithm efficiency . For the BP neural network, Luo Liqiang proposed to use the tapping technology to optimize the model parameters, thereby reducing training time and improved the efficiency of the algorithm by using the single‐component prediction method based on BEP.…”

Section: Neural Network Algorithm Efficiencymentioning

confidence: 99%

Application of artificial neural networks to X‐ray fluorescence spectrum analysis

et al. 2018

X-Ray Spectrometry

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X‐ray fluorescence (XRF) is widely applied as a mature nondestructive testing method, and appropriate improvement of quantitative analysis methods can improve the accuracy of XRF. Artificial neural network is an intelligent information processing system, its developments and application in XRF are reviewed, and representative models (back propagation, radial basis function, genetic algorithm artificial neural network, and others) are discussed in more details in overfitting, generalization, and algorithm efficiency. Potential directions of developing artificial neural network applied in XRF are proposed in this review as a further study.

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Convergence analysis of BP neural networks via sparse response regularization

Cited by 45 publications

References 24 publications

A Novel Heuristic Artificial Neural Network Model for Urban Computing

A Novel Heuristic Artificial Neural Network Model for Urban Computing

Artificial Neural Networks and the Mass Appraisal of Real Estate

Application of artificial neural networks to X‐ray fluorescence spectrum analysis

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