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» On the design of metaheuristic algorithms using fuzzy rules
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FUZZIEEE
2007
IEEE
14 years 4 months ago
Learning Fuzzy Linguistic Models from Low Quality Data by Genetic Algorithms
— Incremental rule base learning techniques can be used to learn models and classifiers from interval or fuzzyvalued data. These algorithms are efficient when the observation e...
Luciano Sánchez, José Otero
BIOSYSTEMS
2007
115views more  BIOSYSTEMS 2007»
13 years 10 months ago
Evolving fuzzy rules to model gene expression
This paper develops an algorithm that extracts explanatory rules from microarray data, which we treat as time series, using genetic programming (GP) and fuzzy logic. Reverse polis...
Ricardo Linden, Amit Bhaya
TFS
2008
157views more  TFS 2008»
13 years 9 months ago
Efficient Self-Evolving Evolutionary Learning for Neurofuzzy Inference Systems
Abstract--This study proposes an efficient self-evolving evolutionary learning algorithm (SEELA) for neurofuzzy inference systems (NFISs). The major feature of the proposed SEELA i...
Cheng-Jian Lin, Cheng-Hung Chen, Chin-Teng Lin
ICC
2007
IEEE
102views Communications» more  ICC 2007»
14 years 4 months ago
Use of Fuzzy Bayesian Clustering to Enhance Generalization Capacity of Radio Network Planning Tool
— To enhance the generalization capacity of a distribution learning method, we propose to use a fuzzy Bayesian framework based on Bayes rules. The precision of the learning resul...
Zakaria Nouir, Berna Sayraç, Benoît F...
MMB
2012
Springer
259views Communications» more  MMB 2012»
12 years 5 months ago
Boosting Design Space Explorations with Existing or Automatically Learned Knowledge
Abstract. During development, processor architectures can be tuned and configured by many different parameters. For benchmarking, automatic design space explorations (DSEs) with h...
Ralf Jahr, Horia Calborean, Lucian Vintan, Theo Un...