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Titlebook: Analysis and Design of Intelligent Systems Using Soft Computing Techniques; Patricia Melin,Oscar Castillo,Witold Pedrycz Book 2007 Springe

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發(fā)表于 2025-3-21 17:20:18 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
期刊全稱(chēng)Analysis and Design of Intelligent Systems Using Soft Computing Techniques
影響因子2023Patricia Melin,Oscar Castillo,Witold Pedrycz
視頻videohttp://file.papertrans.cn/157/156190/156190.mp4
發(fā)行地址Proceedings of the IFSA World Congress 2007 held in Cancun, Mexico June 18- 21.Recent research results in Fuzzy Logic and Soft Computing
學(xué)科分類(lèi)Advances in Intelligent and Soft Computing
圖書(shū)封面Titlebook: Analysis and Design of Intelligent Systems Using Soft Computing Techniques;  Patricia Melin,Oscar Castillo,Witold Pedrycz Book 2007 Springe
影響因子This book comprises a selection of papers from IFSA 2007 on new methods for ana- sis and design of hybrid intelligent systems using soft computing techniques. Soft Computing (SC) consists of several computing paradigms, including fuzzy logic, n- ral networks, and genetic algorithms, which can be used to produce powerful hybrid intelligent systems for solving problems in pattern recognition, time series prediction, intelligent control, robotics and automation. Hybrid intelligent systems that combine several SC techniques are needed due to the complexity and high dimensionality of real-world problems. Hybrid intelligent systems can have different architectures, which have an impact on the efficiency and accuracy of these systems, for this reason it is very important to optimize architecture design. The architectures can combine, in different ways, neural networks, fuzzy logic and genetic algorithms, to achieve the ultimate goal of pattern recognition, time series prediction, intelligent control, or other application areas. This book is intended to be a major reference for scientists and engineers interested in applying new computational and mathematical tools to design hybrid intelli
Pindex Book 2007
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https://doi.org/10.1007/978-3-322-87848-9 of the biometric measures. The response integration method of the modular neural network has the goal of combining the responses of the modules to improve the recognition rate of the individual modules. We show in this paper results of a type-2 fuzzy approach for response integration that improves performance over type-1 fuzzy logic approaches.
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Frühauslese auf Cumarin-Armut beim Steinklee and reconstruction error. We experimentally demonstrate that the kernel-based FCM algorithms do not produce significant improvement over standard FCM for most data sets under investigation It is shown that the kernel-based FCM algorithms appear to be highly sensitive to the selection of the values of the kernel parameters.
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https://doi.org/10.1007/978-3-662-41291-6s to type-2 MFs which gave very good performance. We have performed various experiments in which the evolved type-2 FLC dealt with the uncertainties and resulted in a very good performance that has outperformed its type-1 counterpart as well as the manually designed type-2 FLC.
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Rangkorrelation und Quadrantenkorrelationow a speed increase of one to almost two orders of magnitude for particular cluster configurations. This methodology is particularly important for real time applications such as segmentation of video streams and high throughput problems.
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Evolving Type-2 Fuzzy Logic Controllers for Autonomous Mobile Robotss to type-2 MFs which gave very good performance. We have performed various experiments in which the evolved type-2 FLC dealt with the uncertainties and resulted in a very good performance that has outperformed its type-1 counterpart as well as the manually designed type-2 FLC.
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Interval Type-1 Non-singleton Type-2 TSK Fuzzy Logic Systems Using the Hybrid Training Method RLS-BPill (HSM) and used to predict the transfer bar surface temperature at finishing Scale Breaker (SB) entry zone. Comparative results show the performance of the hybrid learning method (RLS-BP) against the only BP learning method.
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