Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Classes of kernels for machine learning: a statistics perspective
The Journal of Machine Learning Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
IEEE Transactions on Pattern Analysis and Machine Intelligence
Gradient-Based Optimization of Hyperparameters
Neural Computation
Generalized Discriminant Analysis Using a Kernel Approach
Neural Computation
Neural Computation
A new approach to intelligent fault diagnosis of rotating machinery
Expert Systems with Applications: An International Journal
Wavelet based fault detection in analog VLSI circuits using neural networks
Applied Soft Computing
Feature Selection with Kernel Class Separability
IEEE Transactions on Pattern Analysis and Machine Intelligence
Particle swarm optimization with adaptive population size and its application
Applied Soft Computing
Incremental constructive ridgelet neural network
Neurocomputing
Fault diagnosis of pneumatic systems with artificial neural network algorithms
Expert Systems with Applications: An International Journal
An application of PSO technique for harmonic elimination in a PWM inverter
Applied Soft Computing
Simplifying Particle Swarm Optimization
Applied Soft Computing
Chaotic maps based on binary particle swarm optimization for feature selection
Applied Soft Computing
Integrated Learning Particle Swarm Optimizer for global optimization
Applied Soft Computing
Expert Systems with Applications: An International Journal
A novel particle swarm optimization algorithm with adaptive inertia weight
Applied Soft Computing
Generalizing discriminant analysis using the generalized singular value decomposition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Input space versus feature space in kernel-based methods
IEEE Transactions on Neural Networks
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
Face recognition using kernel direct discriminant analysis algorithms
IEEE Transactions on Neural Networks
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Kernel principal component analysis (KPCA) and kernel linear discriminant analysis (KLDA) are two commonly used and effective methods for dimensionality reduction and feature extraction. In this paper, we propose a KLDA method based on maximal class separability for extracting the optimal features of analog fault data sets, where the proposed KLDA method is compared with principal component analysis (PCA), linear discriminant analysis (LDA) and KPCA methods. Meanwhile, a novel particle swarm optimization (PSO) based algorithm is developed to tune parameters and structures of neural networks jointly. Our study shows that KLDA is overall superior to PCA, LDA and KPCA in feature extraction performance and the proposed PSO-based algorithm has the properties of convenience of implementation and better training performance than Back-propagation algorithm. The simulation results demonstrate the effectiveness of these methods.