Between Nonlinearities, Complexity, and Noises: An Application on Portfolio Selection Using Kernel Principal Component Analysis

Peng, Yaohao; Albuquerque, Pedro Henrique Melo; Nascimento, Igor Ferreira do; Machado, João

doi:10.3390/e21040376

Cited by 21 publications

(9 citation statements)

References 91 publications

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“…erefore, the PCA algorithm is used to reduce the dimensions of the original data before model training. More information about PCA is available in [28][29][30]. Under the premise of affecting validity of the data as little as possible, the feature dimension is reduced as much as possible and the training efficiency is improved.…”

Section: Network Fault Data Preprocessingmentioning

confidence: 99%

Early Detection of Network Fault Using Improved Gray Wolf Optimization and Wavelet Neural Network

Pan

Jin

Yang

et al. 2022

Mathematical Problems in Engineering

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To address the problem of diagnostic accuracy and stability degradation caused by random selection of the initial parameters for the wavelet neural network (WNN) fault diagnosis model, this paper proposes a network troubleshooting model based on the improved gray wolf algorithm (IGWO) and the wavelet neural network. First, the convergence factor and policy for the weight update are redesigned in the IGWO algorithm. This study uses a nonlinear convergence factor to balance the global and local search capabilities of the algorithm and dynamically adjusts the weights according to the adaptability of the head wolf α to strengthen its leadership position. Thereafter, the initial weights and biases of the WNN are optimized using the IGWO algorithm. During the backpropagation of the WNN error, momentum factors are introduced to prevent the model from falling into local optimization. Experimental results show that the IGWO algorithm is far better than GWO in terms of convergence speed and convergence accuracy. Furthermore, the average diagnostic accuracy of the IGWO-WNN model on the KDD-CUP99 dataset reaches 99.22%, which is 1.15% higher than that of the WNN model, and the stability of the diagnostic results is significantly improved.

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Section: Network Fault Data Preprocessingmentioning

confidence: 99%

Early Detection of Network Fault Using Improved Gray Wolf Optimization and Wavelet Neural Network

Pan

Jin

Yang

et al. 2022

Mathematical Problems in Engineering

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“…The matrix composed of eigenvectors can be converted into a new space to achieve dimensionality reduction of the original data set. More information about PCA is available in the literature [ 30 , 31 , 32 ].…”

Section: Analyzed States Of the Angle Grindermentioning

confidence: 99%

Ventilation Diagnosis of Angle Grinder Using Thermal Imaging

Głowacz

2021

Sensors

148

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The paper presents an analysis and classification method to evaluate the working condition of angle grinders by means of infrared (IR) thermography and IR image processing. An innovative method called BCAoMID-F (Binarized Common Areas of Maximum Image Differences—Fusion) is proposed in this paper. This method is used to extract features of thermal images of three angle grinders. The computed features are 1-element or 256-element vectors. Feature vectors are the sum of pixels of matrix V or PCA of matrix V or histogram of matrix V. Three different cases of thermal images were considered: healthy angle grinder, angle grinder with 1 blocked air inlet, angle grinder with 2 blocked air inlets. The classification of feature vectors was carried out using two classifiers: Support Vector Machine and Nearest Neighbor. Total recognition efficiency for 3 classes (TRAG) was in the range of 98.5–100%. The presented technique is efficient for fault diagnosis of electrical devices and electric power tools.

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“…This is a summative article with high citations in the local data. Peng et al (2019) discussed the effect of introducing nonlinear interaction and noise filtering into the covariance matrix of the Markowitz portfolio allocation model and conduct an empirical analysis. Bai et al (2019) further compared the performance of the innovative robust portfolio method with the traditional Markowitz method by analyzing the portfolio of China's renewable resources stocks.…”

Section: Local Backward Main Pathmentioning

confidence: 99%

Dynamic changes and multi-dimensional evolution of portfolio optimization

Zhou

Zhu

Chen

et al. 2021

Economic Research-Ekonomska Istraživanja

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Although there has been an increasing number of studies investigate portfolio optimization from different perspectives, few attempts could be found that focus on the development trend and hotspots of this research area. Therefore, it motivates us to comprehensively investigate the development of portfolio optimization research and give some deep insights into this knowledge domain. In this paper, some bibliometric methods are utilized to analyse the status quo and emerging trends of portfolio optimization research on various aspects such as authors, countries and journals. Besides, 'theories', 'models' and 'algorithms', especially heuristic algorithms are identified as the hotspots in the given periods. Furthermore, the evolutionary analysis tends to presents the dynamic changes of the cutting-edge concepts of this research area in the time dimension. It is found that more portfolio optimization studies were at an exploration stage from mean-variance analysis to consideration of multiple constraints. However, heuristic algorithms have become the driving force of portfolio optimization research in recent years. Multidisciplinary analyses and applications are also the main trends of portfolio optimization research. By analysing the dynamic changes and multi-dimensional evolution in recent decades, we contribute to presenting some deep insights of the portfolio optimization research directly, which assists researchers especially beginners to comprehensively learn this research field.

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Between Nonlinearities, Complexity, and Noises: An Application on Portfolio Selection Using Kernel Principal Component Analysis

Cited by 21 publications

References 91 publications

Early Detection of Network Fault Using Improved Gray Wolf Optimization and Wavelet Neural Network

Early Detection of Network Fault Using Improved Gray Wolf Optimization and Wavelet Neural Network

Ventilation Diagnosis of Angle Grinder Using Thermal Imaging

Dynamic changes and multi-dimensional evolution of portfolio optimization

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