Fuzzy sets, uncertainty, and information
Fuzzy sets, uncertainty, and information
Fuzzy sets and fuzzy logic: theory and applications
Fuzzy sets and fuzzy logic: theory and applications
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Foundations of Neuro-Fuzzy Systems
Foundations of Neuro-Fuzzy Systems
Differentiable T-Norms and Related Membership Functions Families and their Application
International Joint Conference, 7th Ibero-American Conference, 15th Brazilian Symposium on AI, IBERAMIA-SBIA 2000, Open Discussion Track Proceedings on AI
Handbook of data mining and knowledge discovery
Handbook of data mining and knowledge discovery
Pattern Recognition Algorithms for Data Mining: Scalability, Knowledge Discovery, and Soft Granular Computing
Rough fuzzy MLP: knowledge encoding and classification
IEEE Transactions on Neural Networks
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A rough evolutionary neuro-fuzzy system for classification and rule generation is proposed. Interactive and differentiable t-norms and t-conorms involving logical neurons in a three-layer perceptron are used. This paper presents the results of application of the methodology based on rough set theory, which initializes the number of hidden nodes and some of the weight values. In search of the smallest network with a good generalization capacity, the genetic algorithms operate on population of individuals composed by integration of dependency rules that will be mapped on networks. Justification of an inferred decision was produced in rule form expressed as the disjunction of conjunctive clauses. The effectiveness of the algorithm is demonstrated on a speech recognition problem. The results are compared with those of fuzzy-MLP and Rough-Fuzzy-MLP, with no logical neuron; the Logical-P, which uses product and probabilistic sum; and other related models.