Intuitionistic Fuzzy Logic Implementation to Assess Purposeful Model Parameters Genesis

• Published in: Recent Contributions in Intelligent Systems Vol. 657; pp. 179 - 203
• Main Authors: Pencheva, Tania, Angelova, Maria
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2016; Springer International Publishing
• Series: Studies in Computational Intelligence
• Subjects: Artificial intelligence; Fermentation process; Genetic algorithm; Intuitionistic fuzzy logic; Parameter identification
• ISBN: 3319414372; 9783319414379
• ISSN: 1860-949X; 1860-9503
• DOI: 10.1007/978-3-319-41438-6_11

In this investigation, intuitionistic fuzzy logic is implemented to derive intuitionistic fuzzy estimations of model parameters of yeast fed-batch cultivation. Two kinds of simple genetic algorithms with operators sequences selection-crossover-mutation and mutation-crossover-selection are here considered, both applied for the purposes of parameter identification of S. cerevisiae fed-batch cultivation. Intuitionistic fuzzy logic overbuilds the results achieved by the application of recently developed purposeful model parameters genesis procedure in order to keep promising results obtained. Behavior of applied algorithms has also been examined at different values of the genetic algorithms parameter generation gap, proven as the most sensitive parameter toward convergence time. Results obtained after the implementation of intuitionistic fuzzy logic for the assessment of algorithms performances have been compared and based on the evaluations in each case the most reliable algorithm has been distinguished.