Engineering drug design using a multi-input multi-output neuro-fuzzy system

Proceedings of the 8th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, SYNASC 2006

Volumn , Issue , 2006, Pages 365-371

  Grosan, Crina ^a   Abraham, Ajith ^b   Ţigan, Ştefan ^c  

^a BABE BOLYAI UNIVERSITY   (Romania)

^b Yonsei University   (South Korea)

^c IULIU HATIEGANU UNIVERSITY OF MEDICINE AND PHARMACY   (Romania)

Author keywords

[No Author keywords available]

Indexed keywords

FEEDBACK CONTROL; FUZZY NEURAL NETWORKS; FUZZY SYSTEMS; LEARNING SYSTEMS; MIMO SYSTEMS; STATISTICAL METHODS;

BOOTSTRAP TECHNIQUE; DRUG FORMULATIONS; FUZZY INFERENCE SYSTEMS; MULTI INPUT MULTI OUTPUT; NEURAL NETWORK LEARNING; NEURO-FUZZY MODEL; NEUROFUZZY SYSTEM; PHARMACEUTICAL RESEARCH;

FUZZY INFERENCE;

EID: 46449114684     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/SYNASC.2006.38     Document Type: Conference Paper

References (6)

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3. T. Hesterberg, S. Monaghan, D. S. Moore, A. Clipson, R. Epstein, Bootstrap Methods and Permutation Tests, W. H. Freeman and Company, New York, 2003
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5. Abraham A., Neuro-Fuzzy Systems: State-of-the-Art Modeling Techniques, Connectionist Models of Neurons, Learning Processes, and Artificial Intelligence, Lecture Notes in Computer Science, Jose Mira and Alberto Prieto (Eds.), Germany, Springer-Verlag, LNCS 2084: 269-276, 2001.
6. Jang, S. R., Sun, C. T. and Mizutani, E., Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence, US, Prentice Hall Inc., 1997.