Parallel genetic algorithms for optimised fuzzy modelling with application to a fermentation process

This paper reports the construction and application of an evolution program to a computational intelligence system used as a software `sensor' in state-estimation and prediction of biomass concentration in a fermentation process. A fuzzy logic system (FLS) is used as a computational engine to `...

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Veröffentlicht in:IEE conference publication S. 123 - 128
1. Verfasser: Soufian, M
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: London IEE 1997
Schlagworte:
Algorithmics. Computability. Computer arithmetics
Applied sciences
Artificial intelligence
Computer science; control theory; systems
Connectionism. Neural networks
Control applications in food processing industries
Exact sciences and technology
Food industry
Formal logic
Industrial applications of IT
Industrial processes
Learning and adaptive systems
Manufacturing and industrial administration
Neural nets
Optimisation
Optimisation techniques
Parallel programming and algorithm theory
Systems analysis and programming
Theoretical computing
parallel algorithms
software prototyping
fermentation process
fuzzy logic
evolution program
biomass concentration
parallel genetic algorithms
genetic algorithms
food processing industry
graphical fit
artificial neural networks
squared errors
computational engine
optimised fuzzy modelling
Levenberg-Marquardt second-order nonlinear optimisation
fermentation
state estimation
fuzzy logic system
neural nets
Parallel algorithm
Estimation
Prediction
Neural network
Experimental study
Fermentation
Optimization
Fuzzy logic
System performance
Genetic algorithm
Data structure
Artificial intelligence
Intelligent system
ISBN:9780852966938, 0852966938
ISSN:0537-9989
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Beschreibung
Zusammenfassung:This paper reports the construction and application of an evolution program to a computational intelligence system used as a software `sensor' in state-estimation and prediction of biomass concentration in a fermentation process. A fuzzy logic system (FLS) is used as a computational engine to `infer' the production of biomass from variables easily measured on-line. For this purpose, genetic algorithms (GAs) are employed to train and tune the desired parameters of the fuzzy logic system. It is shown that the fuzzy logic system, which was tuned by two genetic algorithms implemented in parallel, produces better results in prediction of biomass concentration. The mean sum of squared errors and graphical fit are used to compare the performance of the genetically optimised FLS with artificial neural networks (ANN), which is trained using Levenberg-Marquardt second-order nonlinear optimisation method.
ISBN:9780852966938
0852966938
ISSN:0537-9989
DOI:10.1049/cp:19971167