Efficient reinforcement learning through symbiotic evolution
Machine Learning - Special issue on reinforcement learning
Hybrid Evolutionary Search Method Based on Clusters
IEEE Transactions on Pattern Analysis and Machine Intelligence
A comparison of linear genetic programming and neural networks inmedical data mining
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Information Technology in Biomedicine
Genetic reinforcement learning through symbiotic evolution forfuzzy controller design
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A systematic neuro-fuzzy modeling framework with application tomaterial property prediction
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Combined numerical and linguistic knowledge representation and its application to medical diagnosis
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Self-organized fuzzy system generation from training examples
IEEE Transactions on Fuzzy Systems
A fuzzy-logic-based approach to qualitative modeling
IEEE Transactions on Fuzzy Systems
Subsethood-product fuzzy neural inference system (SuPFuNIS)
IEEE Transactions on Neural Networks
An introduction to simulated evolutionary optimization
IEEE Transactions on Neural Networks
Fault diagnosis of an air-handling unit system using a dynamic fuzzy-neural approach
ICAISC'10 Proceedings of the 10th international conference on Artificial intelligence and soft computing: Part I
A “learning from models” cognitive fault diagnosis system
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part II
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This paper presents a symbiotic evolution-based fuzzy-neural diagnostic system (SE-FNDS) for fault diagnosis of propeller-shaft marine propulsion systems. The SE-FNDS combination of fuzzy modeling, back-propagation training and symbiotic evolution function auto-generates its own optimal fuzzy-neural architecture, a significant advantage over previous time-consuming manual parameter determination. Four hundred samples from a test propeller-shaft system are taken over a range of 100-500rpm, during normal and experimentally induced faulty operation. This database is applied as input/output rule generation and training data for the fuzzy-neural network. Comparison of system construction time and diagnostic accuracy is made by applying the same database to SE-FNDS and four traditional systems. Compared to traditional methods, diagnostic decisions from SE-FNDS show 94.17% agreement with real conditions and less CPU time for system construction. Two nonlinear function approximations are also used to demonstrate the proposed system. The presented design is useful as a core module for more advanced computer-assisted diagnostic systems and for direct application in marine propulsion systems.