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Advanced datadriven approaches for modelling and classification: with applications to automotive engine fault detection and pol,Used
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In this book, the Fast Recursive Algorithm (FRA) and TwoStage Selection (TSS) methods proposed by Prof. Li and Prof. Irwin have been improved to integrate Bayesian regularisation to prevent overfitting and leaveoneout cross validation for automatic model construction. To further enhance model generalization capability, some heuristic methods were also embedded in the twostage selection to optimize the nonlinear parameters involved in subset model construction. These include Particle Swarm Optimization (PSO), Defferential Evolution (DE), and Extreme Learning Machine (ELM). The effectiveness and efficiency of all these advanced methods have been confirmed on both wellknown benchmarks and real world data sets from automotive engine and polymer extrusion applications.
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