A Transductive Neuro-Fuzzy Controller: Application to a Drilling Process
Autor: | Rodolfo E. Haber, Agustín Gajate, Pastora Vega, J. R. Alique |
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Rok vydání: | 2010 |
Předmět: |
Transduction (machine learning)
Computer Networks and Communications Computer science Models Neurological Internal model Inference Machine learning computer.software_genre Fuzzy logic Fuzzy Logic Artificial Intelligence Animals Cluster Analysis Humans Process control Neurons Adaptive neuro fuzzy inference system business.industry General Medicine Fuzzy control system Computer Science Applications ComputingMethodologies_PATTERNRECOGNITION Control system Algorithm design Neural Networks Computer Artificial intelligence business computer Algorithms Software |
Zdroj: | IEEE Transactions on Neural Networks. 21:1158-1167 |
ISSN: | 1941-0093 1045-9227 |
DOI: | 10.1109/tnn.2010.2050602 |
Popis: | Recently, new neuro-fuzzy inference algorithms have been developed to deal with the time-varying behavior and uncertainty of many complex systems. This paper presents the design and application of a novel transductive neuro-fuzzy inference method to control force in a high-performance drilling process. The main goal is to study, analyze, and verify the behavior of a transductive neuro-fuzzy inference system for controlling this complex process, specifically addressing the dynamic modeling, computational efficiency, and viability of the real-time application of this algorithm as well as assessing the topology of the neuro-fuzzy system (e.g., number of clusters, number of rules). A transductive reasoning method is used to create local neuro-fuzzy models for each input/output data set in a case study. The direct and inverse dynamics of a complex process are modeled using this strategy. The synergies among fuzzy, neural, and transductive strategies are then exploited to deal with process complexity and uncertainty through the application of the neuro-fuzzy models within an internal model control (IMC) scheme. A comparative study is made of the adaptive neuro-fuzzy inference system (ANFIS) and the suggested method inspired in a transductive neuro-fuzzy inference strategy. The two neuro-fuzzy strategies are evaluated in a real drilling force control problem. The experimental results demonstrated that the transductive neuro-fuzzy control system provides a good transient response (without overshoot) and better error-based performance indices than the ANFIS-based control system. In particular, the IMC system based on a transductive neuro-fuzzy inference approach reduces the influence of the increase in cutting force that occurs as the drill depth increases, reducing the risk of rapid tool wear and catastrophic tool breakage. |
Databáze: | OpenAIRE |
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