An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Recursive Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Discovery of Fuzzy Time Series for Financial Prediction
IEEE Transactions on Knowledge and Data Engineering
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Similarity based method for manufacturing process performance prediction and diagnosis
Computers in Industry
Semiconductor Manufacturing Handbook
Semiconductor Manufacturing Handbook
Bearing performance degradation assessment using locality preserving projections
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Physics for Scientists and Engineers with Modern, Hybrid (with Enhanced WebAssign Homework and eBook LOE Printed Access Card for Multi Term Math and Science)
Prediction of chaotic time series based on the recurrent predictor neural network
IEEE Transactions on Signal Processing
Wavelet-based combined signal filtering and prediction
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Recurrent neural networks and robust time series prediction
IEEE Transactions on Neural Networks
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In this paper, a new method is proposed that is capable of predicting system condition by comparing the similarity of the most recent performance signatures with the known degradation patterns available in the historical records. For predicting the future performance, the similarities of the current performance signatures to each known degradation pattern are utilized in an analytically tractable manner to slant the prediction distributions toward most similar past degradation patterns. The newly proposed method was applied to prediction of sensor signatures coming from an industrial plasma enhanced chemical vapor deposition (PECVD) tool operating in a major semiconductor manufacturing fab. Results showed that the proposed method significantly improves the long-term time series prediction accuracy in terms of mean squared errors over the traditional autoregressive moving average (ARMA) model and additionally showed comparable mean squared prediction errors to another recently introduced similarity-based algorithm for long-term prediction of non-linear and non-stationary time series. However, the analytical structure of the method proposed in this paper enables computation of the prediction distributions an order of magnitude faster.