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Titlebook: Recent Advances on Hybrid Approaches for Designing Intelligent Systems; Oscar Castillo,Patricia Melin,Janusz Kacprzyk Book 2014 Springer I

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樓主: Twinge
31#
發(fā)表于 2025-3-26 22:00:36 | 只看該作者
Jesus Soto,Patricia Melinse results decouple Europeanized representation from support of the European integration project or the EU’s policies. This challenges an implicit assumption in EU studies: It is far from clear that those in favor of the EU are always the drivers of Europeanization processes. When it comes to the st
32#
發(fā)表于 2025-3-27 03:30:14 | 只看該作者
strings of European integration theory, history, economics and political sciences, which are clearly brought together into a coherent analytical discourse. Its strength is the interdisciplinary interaction between politics and economics, as well as theoretical and practical issues which are of high
33#
發(fā)表于 2025-3-27 07:48:54 | 只看該作者
1860-949X g and hybrid intelligent systems for control, mobile robotic.This book describes recent advances on hybrid intelligent systems using soft computing techniques for diverse areas of application, such as intelligent control and robotics, pattern recognition, time series prediction and optimization comp
34#
發(fā)表于 2025-3-27 10:49:30 | 只看該作者
Neural Network with Fuzzy Weights Using Type-1 and Type-2 Fuzzy Learning with Gaussian Membership Fud. The proposed method is based on recent methods that handle weight adaptation and especially fuzzy weights. In this work neural networks with type-1 fuzzy weights or type-2 fuzzy weights are presented. The proposed approach is applied to the case of Mackey–Glass time series prediction.
35#
發(fā)表于 2025-3-27 14:30:33 | 只看該作者
36#
發(fā)表于 2025-3-27 19:39:08 | 只看該作者
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發(fā)表于 2025-3-28 00:33:21 | 只看該作者
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發(fā)表于 2025-3-28 06:06:30 | 只看該作者
39#
發(fā)表于 2025-3-28 06:17:52 | 只看該作者
40#
發(fā)表于 2025-3-28 10:26:44 | 只看該作者
Ensemble Neural Network Optimization Using the Particle Swarm Algorithm with Type-1 and Type-2 Fuzzyy Integration. The time series that is being considered in this work is the Mackey-Glass benchmark time series. Simulation results show that the ensemble approach produces good prediction of the Mackey-Glass time series.
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