An integrated model based on feedforward neural network and Taylor expansion for indicator correlation elimination

Guo, Wei; Qiu, Han; Liu, Zimian; Zhu, Junhu; Wang, Qingxian

doi:10.3233/ida-215955

Cited by 2 publications

(1 citation statement)

References 44 publications

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“…Some researchers have used the Taylor expansion of log-likelihood functions to reach an analytical approximation of Jackknife connection error, which needs lower computational requirements [17]. Guo et al, proposed an index correlation elimination algorithm based on a feedforward neural network and Taylor expansion, which made up for the defect that most evaluation algorithms do not consider the independence between indices [18]. In addition, Taylor expansion has been applied to image space transformation, which can convert the discrete space of images into a continuous linear space, extending the images in an abstract way [19].…”

Section: Methodsmentioning

confidence: 99%

Identifying Core Wavelengths of Oil Tree’s Hyperspectral Data by Taylor Expansion

et al. 2023

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The interference of background noise leads to the extremely high spatial complexity of hyperspectral data. Sensitive band selecting is an important task to minimize or eliminate the influence of non-target elements. In this study, Taylor expansion is innovatively used to identify core wavelengths/bands of hyperspectral data. Unlike other traditional methods, this proposed Taylor-CC method considers more local and global information of spectral function to estimate the linear/nonlinear correlation between two wavelengths. Using samples of hyperspectral data with a wavelength range of 350–2500 nm and SPAD for Camellia oleifera, this Taylor-CC method is compared with the traditional PCC method derived from the Pearson correlation coefficient. Using the 240 samples with their different 57 core wavelengths identified by the Taylor-CC method and PCC method, three machine models (i.e., random forest-RF, linear regression-LR, and artificial neural network-ANN) are trained to compare their performances. Their results show that the correlation matrix from the Taylor-CC method represents a clear diagonal pattern with near zero values at most locations away from the diagonal, and all three models confirm that the Taylor-CC method is superior to the PCC method. Moreover, the SPAD spectral response relationship based on machine learning algorithms is constructed, and ANN is the best prediction performance among the three models when using the core wavelengths identified by the Taylor-CC method. The Taylor-CC method proposed in this study not only lays a mathematical foundation for the next analysis of the response mechanism between spectral characteristics and nutrient content of Camellia leaf, but also provides a new idea for the correlation analysis of adjacent spectral bands for hyperspectral signals in many applications.

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Section: Methodsmentioning

confidence: 99%

Identifying Core Wavelengths of Oil Tree’s Hyperspectral Data by Taylor Expansion

et al. 2023

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Training Feedforward Neural Networks Using an Enhanced Marine Predators Algorithm

Zhang

Yang

2023

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The input layer, hidden layer, and output layer are three models of the neural processors that make up feedforward neural networks (FNNs). Evolutionary algorithms have been extensively employed in training FNNs, which can correctly actualize any finite training sample set. In this paper, an enhanced marine predators algorithm (MPA) based on the ranking-based mutation operator (EMPA) was presented to train FNNs, and the objective was to attain the minimum classification, prediction, and approximation errors by modifying the connection weight and deviation value. The ranking-based mutation operator not only determines the best search agent and elevates the exploitation ability, but it also delays premature convergence and accelerates the optimization process. The EMPA integrates exploration and exploitation to mitigate search stagnation, and it has sufficient stability and flexibility to acquire the finest solution. To assess the significance and stability of the EMPA, a series of experiments on seventeen distinct datasets from the machine learning repository of the University of California Irvine (UCI) were utilized. The experimental results demonstrated that the EMPA has a quicker convergence speed, greater calculation accuracy, higher classification rate, strong stability and robustness, which is productive and reliable for training FNNs.

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An integrated model based on feedforward neural network and Taylor expansion for indicator correlation elimination

Cited by 2 publications

References 44 publications

Identifying Core Wavelengths of Oil Tree’s Hyperspectral Data by Taylor Expansion

Identifying Core Wavelengths of Oil Tree’s Hyperspectral Data by Taylor Expansion

Training Feedforward Neural Networks Using an Enhanced Marine Predators Algorithm

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