Hardware-Based Principal Component Analysis for Hybrid Neural Network Trained by Particle Swarm Optimization on a Chip

2019 ◽  
Vol E102.A (10) ◽  
pp. 1374-1382
Author(s):  
Tuan Linh DANG ◽  
Yukinobu HOSHINO
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Principal Component ◽  
Component Analysis ◽  
Swarm Optimization ◽  
Hybrid Neural Network

Automatically Terminated Particle Swarm Optimization with Principal Component Analysis

2015 ◽  
Vol 14 (01) ◽  
pp. 171-194 ◽  
Author(s):  
Bun Theang Ong ◽  
Masao Fukushima
Keyword(s):  
Principal Component Analysis ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Principal Component ◽  
Component Analysis ◽  
Convergence Speed ◽  
Swarm Optimization ◽  
Termination Criteria ◽  
Hybrid Particle Swarm Optimization ◽  
Speed Accuracy

A hybrid Particle Swarm Optimization (PSO) that features an automatic termination and better search efficiency than classical PSO is presented. The proposed method is combined with the so-called "Gene Matrix" to provide the search with a self-check in order to determine a proper termination instant. Its convergence speed and reliability are also increased by the implementation of the Principal Component Analysis (PCA) technique and the hybridization with a local search method. The proposed algorithm is denominated as "Automatically Terminated Particle Swarm Optimization with Principal Component Analysis" (AT-PSO-PCA). The computational experiments demonstrate the effectiveness of the automatic termination criteria and show that AT-PSO-PCA enhances the convergence speed, accuracy and reliability of the PSO paradigm. Furthermore, comparisons with state-of-the-art evolutionary algorithms (EA) yield competitive results even under the automatically detected termination instant.

scholarly journals Fault diagnosis of bearing based on the kernel principal component analysis and optimized k-nearest neighbour model

2017 ◽  
Vol 36 (4) ◽  
pp. 354-365 ◽  
Author(s):  
Shaojiang Dong ◽  
Tianhong Luo ◽  
Li Zhong ◽  
Lili Chen ◽  
Xiangyang Xu
Keyword(s):  
Principal Component Analysis ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Principal Component ◽  
Component Analysis ◽  
Kernel Principal Component Analysis ◽  
Frequency Domain Method ◽  
Nearest Neighbour ◽  
Swarm Optimization ◽  
Time Frequency

Aiming to identify the bearing faults level effectively, a new method based on kernel principal component analysis and particle swarm optimization optimized k-nearest neighbour model is proposed. First, the gathered vibration signals are decomposed by time–frequency domain method, i.e., local mean decomposition; as a result, the product functions decomposed from the original signal are derived. Then, the entropy values of the product functions are calculated by Shannon method, which will work as the input features for k-nearest neighbour model. The kernel principal component analysis model is used to reduce the dimension of the features, and then the k-nearest neighbour model which was optimized by the particle swarm optimization method is used to identify the bearing fault levels. Case of test and actually collected signal are analysed. The results validate the effectiveness of the proposed algorithm.

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