By Lotfi A. Zadeh (auth.), Patricia Melin, Oscar Castillo, Eduardo Gomez Ramírez, Janusz Kacprzyk, Witold Pedrycz (eds.)
This booklet contains a variety of papers from IFSA 2007 on new equipment for research and layout of hybrid clever structures utilizing smooth computing strategies. delicate Computing (SC) includes numerous computing paradigms, together with fuzzy common sense, neural networks, and genetic algorithms, that are used to supply robust hybrid clever structures for fixing difficulties in development reputation, time sequence prediction, clever keep an eye on, robotics and automation. Hybrid clever structures that mix numerous SC strategies are wanted as a result complexity and excessive dimensionality of real-world difficulties. Hybrid clever platforms may have various architectures, that have an influence at the potency and accuracy of those structures, accordingly it is important to to optimize structure layout. The architectures can mix, in numerous methods, neural networks, fuzzy common sense and genetic algorithms, to accomplish the last word aim of trend attractiveness, time sequence prediction, clever keep an eye on, or different program parts.
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Extra resources for Analysis and Design of Intelligent Systems using Soft Computing Techniques
To 34 S. Huh et al. implement the soft-remote control system, important features of the users’ gesture are selected by using rough set theory. By observing the characteristic of selected feature for each gesture, rules are generated for each case. Finally, the proposed approach is applied to control home appliances, and the preliminary experimental results show the feasibility and prospect. Acknowledgement This work is fully supported by the SRC/ERC program of MOST/KOSEF (Grant #R11-1999-008). References 1.
Results of the Type-2 Fuzzy System with Triangular Membership Functions In Table 2 we show the results for 15 trainings of the modular neural network. In each row of this table we can appreciate the recognition rate with the type-2 fuzzy sytem. We can appreciate that in 8 out of 15 cases, a 100% recognition rate was achieved. A Method for Response Integration in Modular Neural Networks 13 The fuzzy systems with worst results for the modular neural network were the ones with Gaussian and Trapezoidal membership functions.
However, manually designing the type-2 Membership Functions (MFs) for an interval type-2 FLC is a difficult task. This paper will present a Genetic Algorithm (GA) based architecture to evolve the type-2 MFs of interval type-2 FLCs used for mobile robots. The GA based system converges after a small number of iterations 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.