Data mining–based hierarchical cooperative coevolutionary algorithm for TSK-type neuro-fuzzy networks design

This study proposes a data mining–based hierarchical cooperative coevolutionary algorithm (DMHCCA) for TSK-type neuro-fuzzy networks design. The proposed DMHCCA consists of two-level evolutions: the neuro-level evolution (NULE) and the network-level evolution (NWLE). In NULE, a data mining–based evo...

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Veröffentlicht in:Neural computing & applications Jg. 23; H. 2; S. 485 - 498
Hauptverfasser: Hsu, Chi-Yao, Lin, Sheng-Fuu, Chang, Jyun-Wei
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Springer London 01.08.2013
Springer
Schlagworte:
Applied sciences
Artificial Intelligence
Combinatorics
Combinatorics. Ordered structures
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Computer science; control theory; systems
Data Mining and Knowledge Discovery
Designs and configurations
Exact sciences and technology
Experimental design
Image Processing and Computer Vision
Learning and adaptive systems
Mathematics
Original Article
Probability and statistics
Probability and Statistics in Computer Science
Sciences and techniques of general use
Statistics
Network-level evolution
Data mining–based evolutionary learning algorithm
Hierarchical cooperative coevolutionary algorithm
Neuro-level evolution
Three-dimensional surface alignment
Network structure
Neural computation
Three-dimensional calculations
Evolutionary algorithm
Neural network
Data mining
Surface
Alignment
Experimental result
Neuron
Data mining―based evolutionary learning algorithm
System performance
Experimental design
Learning algorithm
ISSN:0941-0643, 1433-3058
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Zusammenfassung:This study proposes a data mining–based hierarchical cooperative coevolutionary algorithm (DMHCCA) for TSK-type neuro-fuzzy networks design. The proposed DMHCCA consists of two-level evolutions: the neuro-level evolution (NULE) and the network-level evolution (NWLE). In NULE, a data mining–based evolutionary learning algorithm is utilized to evolve neurons. The good combinations of neurons evolved in NULE are reserved for being the initial populations of NWLE. In NWLE, the initial population are mated and mutated to produce new structure of networks. Similar to NULE, the good neurons of evolved network in NWLE are inserted into the NULE. Thus, by interactive two-level evolutions, the neurons and structure of network can be evolved locally and globally, respectively. Simulation results using DMHCCA are reported and compared with other existing models. Application of DMHCCA to a three-dimensional (3D) surface alignment task is also described, and experimental results are presented better performance than other alignment systems.
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-012-0943-0