17 lines
2.4 KiB
BibTeX
17 lines
2.4 KiB
BibTeX
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@article{penedo_hybrid_2012,
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title = {Hybrid {Incremental} {Modeling} {Based} on {Least} {Squares} and {Fuzzy} \${K}\$-{NN} for {Monitoring} {Tool} {Wear} in {Turning} {Processes}},
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volume = {8},
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issn = {1941-0050},
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doi = {10.1109/TII.2012.2205699},
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abstract = {There is now an emerging need for an efficient modeling strategy to develop a new generation of monitoring systems. One method of approaching the modeling of complex processes is to obtain a global model. It should be able to capture the basic or general behavior of the system, by means of a linear or quadratic regression, and then superimpose a local model on it that can capture the localized nonlinearities of the system. In this paper, a novel method based on a hybrid incremental modeling approach is designed and applied for tool wear detection in turning processes. It involves a two-step iterative process that combines a global model with a local model to take advantage of their underlying, complementary capacities. Thus, the first step constructs a global model using a least squares regression. A local model using the fuzzy k-nearest-neighbors smoothing algorithm is obtained in the second step. A comparative study then demonstrates that the hybrid incremental model provides better error-based performance indices for detecting tool wear than a transductive neurofuzzy model and an inductive neurofuzzy model.},
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number = {4},
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journal = {IEEE Transactions on Industrial Informatics},
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author = {Penedo, Francisco and Haber, Rodolfo E. and Gajate, Agustín and del Toro, Raúl M.},
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month = nov,
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year = {2012},
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note = {Conference Name: IEEE Transactions on Industrial Informatics},
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keywords = {Computational modeling, Mathematical model, complex processes, computerised monitoring, Data models, error-based performance indices, Fuzzy \$k\$-nearest-neighbors, fuzzy k-NN method, fuzzy neural nets, fuzzy set theory, Fuzzy systems, fuzzy-nearest-neighbors smoothing algorithm, hybrid incremental modelling, hybrid model, inductive neurofuzzy model, iterative methods, least squares approximations, least squares regression, linear regression, machine tools, Machining, machining processes, monitoring systems, monitoring tool wear detection, quadratic regression, regression analysis, tool wear, transductive neurofuzzy model, turning (machining), turning processes, two-step iterative process, wear},
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pages = {811--818}
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}
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