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
A simple but powerful heuristic method for generating fuzzy rules from numerical data
Fuzzy Sets and Systems
Fuzzy evolutionary computation
Fuzzy evolutionary computation
Fuzzy Modeling for Control
Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
Asynchronous Parallel Pattern Search for Nonlinear Optimization
SIAM Journal on Scientific Computing
The Effect of a Dynamical Layer in Neural Network Prediction of Biomass in a Fermentation Process
IEA/AIE '98 Proceedings of the 11th international conference on Industrial and engineering applications of artificial intelligence and expert systems: methodology and tools in knowledge-based systems
Fed-batch fermentation controller design with evolutionary computation
CSTST '08 Proceedings of the 5th international conference on Soft computing as transdisciplinary science and technology
Semantic constraints for membership function optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Hi-index | 0.00 |
In current study, the ability of computational intelligence methods to tackle modern control problems is under observation. In particular, two computational intelligence techniques belonging to different algorithm families are reviewed, refined and applied to a benchmark fed-batch fermentation process – a linguistic inversion method that designs the controller through automated analysis of information encapsulated in fuzzy linguistic rules of the process model and an evolutionary computation approach that complements its search capabilities with a human critic that guides the optimisation process. The results in terms of productivity measures that compare very favourably to reported results available from literature strongly suggest that in conditions (i.e. presence of noise, parameter variation and randomness) that resemble real-life situations, approximate techniques have an edge over more or less conventional optimisation methods as being more robust and more effective in knowledge discovery.