Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Combining GP operators with SA search to evolve fuzzy rule based classifiers
Information Sciences: an International Journal - Recent advances in genetic fuzzy systems
Obtaining transparent models of chaotic systems with multi-objective simulated annealing algorithms
Information Sciences: an International Journal
International Journal of Approximate Reasoning
Taximeter verification using imprecise data from GPS
Engineering Applications of Artificial Intelligence
A fuzzy logic based efficient energy saving approach for domestic heating systems
Integrated Computer-Aided Engineering
Editorial: Hybrid learning machines
Neurocomputing
Genetic learning of fuzzy rules based on low quality data
Fuzzy Sets and Systems
C-PSA: Constrained Pareto simulated annealing for constrained multi-objective optimization
Information Sciences: an International Journal
Editorial: Hybrid intelligent algorithms and applications
Information Sciences: an International Journal
Analysing the low quality of the data in lighting control systems
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part I
Interval-valued GA-P algorithms
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Advocating the Use of Imprecisely Observed Data in Genetic Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Comparison of fuzzy functions for low quality data GAP algorithms
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part II
Hi-index | 0.01 |
Improving energy efficiency in buildings represents one of the main challenges faced by engineers. In fields like lighting control systems, the effect of low quality sensors compromises the control strategy and the emergence of new technologies also degrades the data quality introducing linguistic values. This research analyzes the aforementioned problem and shows that, in the field of lighting control systems, the uncertainty in the measurements gathered from sensors should be considered in the design of control loops. To cope with this kind of problems Hybrid Intelligent methods will be used. Moreover, a method for learning equation-based white box models with this low quality data is proposed. The equation-based models include a representation of the uncertainty inherited in the data. Two different evolutive algorithms are use for learning the models: the well-known NSGA-II genetic algorithm and a multi-objective simulated annealing algorithm hybridized with genetic operators. The performance of both algorithms is found valid to evolve this learning process. This novel approach is evaluated with synthetic problems.