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

Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence



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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 ebook
Page: 640
Publisher: Prentice Hall
Format: djvu
ISBN: 0132610663, 9780132610667


الگوریتم ژنتیک - Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence - وبلاگی برای من. Tags:Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence, tutorials, pdf, djvu, chm, epub, ebook, book, torrent, downloads, rapidshare, filesonic, hotfile, fileserve. Jyh-Shing Roger Jang, Chuen-Tsai Sun & Eiji Mizutani, “Neuro Fuzzy and Soft Computing-A Computational Approach to Learning and Machine Intelligence”, Prentice Hall of India, 2004. 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 English | 1997-09-26 | ISBN: 0132610663 | 614 pages | PDF | 32.47 mbcentercenter Neuro-Fuzzy. Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence (Matlab Curriculum Series). 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. 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. 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. 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.

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