Structure identification of fuzzy model
Fuzzy Sets and Systems
Using Genetic Algorithms for Concept Learning
Machine Learning - Special issue on genetic algorithms
Essentials of Fuzzy Modeling and Control
Essentials of Fuzzy Modeling and Control
SIA: A Supervised Inductive Algorithm with Genetic Search for Learning Attributes based Concepts
ECML '93 Proceedings of the European Conference on Machine Learning
A learning system based on genetic adaptive algorithms
A learning system based on genetic adaptive algorithms
Software Aging Prediction Model Based on Fuzzy Wavelet Network with Adaptive Genetic Algorithm
ICTAI '06 Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence
Design of a Gain-Scheduling Anti-Swing Controller for Tower Cranes Using Fuzzy Clustering Techniques
CIMCA '06 Proceedings of the International Conference on Computational Inteligence for Modelling Control and Automation and International Conference on Intelligent Agents Web Technologies and International Commerce
Engineering Applications of Artificial Intelligence
Fault detection and fuzzy rule extraction in AC motors by a neuro-fuzzy ART-based system
Engineering Applications of Artificial Intelligence
Forecasting time series with genetic fuzzy predictor ensemble
IEEE Transactions on Fuzzy Systems
SLAVE: a genetic learning system based on an iterative approach
IEEE Transactions on Fuzzy Systems
A proposal for improving the accuracy of linguistic modeling
IEEE Transactions on Fuzzy Systems
Automaton based on fuzzy clustering methods for monitoring industrial processes
Engineering Applications of Artificial Intelligence
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The efficiency of material handling system requires an automation on the different levels of control and supervision to keep availability of the material handling devices for fast, safety and precise transferring materials, as well as to reduce the maintenance cost, which is involved by enhancing the productivity of manufacturing process. In this paper, evolutionary-based algorithm for fuzzy logic-based data-driven predictive model of time between failures (TBF) and adaptive crane control system design is proposed. The heuristic searching strategy combining the arithmetical crossover, uniform and non-uniform mutation and deletion/insertion mutation is developed for optimizing the rules base (RB) and tuning the triangular-shaped membership functions to increase the efficiency and accuracy of a fuzzy rule-based system (FRBS). The evolutionary algorithm (EA) was employed to design a fuzzy predictive model based on the historical data of operational states monitored between the failures of the laboratory scaled overhead traveling crane electronic equipment. The fuzzy predictive model of TBF was implemented in the supervisory system created for supporting decision-making process through forecasting upcoming failure and delivering the user-defined maintenance strategies. The effectiveness of EA was also verified through designing a Takagi-Sugeno-Kang (TSK) fuzzy controller in the anti-sway crane control system.