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Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence Chuen-Tsai Sun, Eiji Mizutani, Jyh-Shing Roger Jang
Publisher: Prentice Hall

Currently, a shift from traditional statistical PCA- / PLS-based techniques to more advanced approaches, like Artificial Neural Networks, kernel-based methods, Gaussian processes, Neuro-Fuzzy Systems can currently be observed in the field of soft sensor development. Computational Methods in Surface and Colloid Science Borowko M. Jang J-SR: Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence. Based on this approach, a fuzzy inference system can be automatically built from practical data .. Hand DJ: Discrimination and classification. Jang JSR, Sun CT, Mizutani E: Neuro-fuzzy and soft computing: a Computational approach to learning and machine intelligence. Neuro – Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence Jang, Jyh-Shing Roger ; Sun, Chuen- A Text Book of Production Technology – II Khanna, O.P. Upper Saddle River NJ: Prenctice Hall; 1997. Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence. Neuro-Fuzzy and Soft Computing A Computational Approach to Learning and Machine Intelligence - Jyh-Shing Roger Jang Simulating Continuous Fuzzy Systems - James J. (ed.) 2000 M.Dekker Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence Jang J.-S.R., Sun C.-T., Mizutani E. Sugeno M: Fuzzy measures and fuzzy integrals: a survey. The achievement of EDM process is affected by many input parameters; therefore, the computational relations between the output responses and controllable input parameters must be known. To make this model selection procedure convenient for clinical use, a learning technique based on neuro-fuzzy systems originally proposed for intelligence control was used for the current study. Some recent publications also demonstrate the increasing popularity of computational intelligence and machine learning concepts like ensemble methods, local learning and meta-learning in soft sensors. However, the proper selection of these Because of the advantages of the artificial intelligence systems, many researchers studied to find the relationships between input and output parameters in EDM process by using soft computing techniques.