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Genetically Optimized Hybrid Fuzzy Set-based Polynomial Neural Networks with Polynomial and Fuzzy Polynomial Neurons.

, , and . Int. J. Fuzzy Log. Intell. Syst., 5 (4): 327-332 (2005)

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Adversarial Dropout for Supervised and Semi-supervised Learning., , , and . CoRR, (2017)A genetic approach to modeling fuzzy systems based on information granulation and successive generation-based evolution method., , and . Simul. Model. Pract. Theory, 15 (9): 1128-1145 (2007)Identification of Fuzzy Systems with the Aid of Genetic Fuzzy Granulation., , , and . IWANN, volume 3512 of Lecture Notes in Computer Science, page 391-398. Springer, (2005)Consecutive Identification of ANFIS-Based Fuzzy Systems with the Aid of Genetic Data Granulation., , and . ISNN (2), volume 3972 of Lecture Notes in Computer Science, page 815-820. Springer, (2006)LBG-Based Non-fuzzy Inference System for Nonlinear Process., , , , and . FCC, volume 301 of Lecture Notes in Electrical Engineering, page 111-117. Springer, (2014)Design of interval type-2 fuzzy neural networks and their optimization using real-coded genetic algorithms., , and . FUZZ-IEEE, page 2013-2018. IEEE, (2009)Adversarial Dropout for Supervised and Semi-Supervised Learning., , , and . AAAI, page 3917-3924. AAAI Press, (2018)Methodological Identification of Information Granules-Based Fuzzy Systems by Means of Genetic Optimization., , and . RSCTC, volume 4259 of Lecture Notes in Computer Science, page 467-476. Springer, (2006)Optimization of Non-fuzzy Neural Networks Based on Crisp Rules in Scatter Partition., , , , and . FCC, volume 301 of Lecture Notes in Electrical Engineering, page 103-109. Springer, (2014)Text augmented automatic statistician for predicting approval rates of politicians., , and . SMC, page 954-959. IEEE, (2017)