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Hardware Implementation of Karnik-Mendel Algorithm for Interval Type-2 Fuzzy Sets and Systems

机译:区间2型模糊集和系统的Karnik-Mendel算法的硬件实现

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The trend to accelerate the learning process in neural and fuzzy systems has led to the design of hardware implementations of different types of algorithms. In this paper we explore type-2 fuzzy logic systems acceleration, which can be applied to fuzzy logic control methods, signal processing, etc. Due to the three dimensional membership functions in the input of the system, different algorithms for the output processing stage have been developed. In order to have a fast response in type-2 fuzzy logic systems, in this paper we explore the Karnik-Mendel algorithms (KM), which are used to calculate the centroid at the output processing stage of the interval type-2 fuzzy system, through the application of iterative procedures. Because of the computation complexity of the iterative process, we propose a Hardware implementation of the KM algorithm using a High Level Synthesis tool, making possible to explore different types of implementation in order to obtain a significant reduction in computation time, and a reduction in hardware resources.
机译:加速神经和模糊系统中学习过程的趋势已导致设计了不同类型算法的硬件实现。在本文中,我们探讨了可用于模糊逻辑控制方法,信号处理等的类型2模糊逻辑系统加速。由于系统输入中具有三维隶属函数,因此在输出处理阶段存在不同的算法已开发。为了在2型模糊逻辑系统中获得快速响应,本文探讨了Karnik-Mendel算法(KM),该算法用于计算区间2型模糊系统的输出处理阶段的质心,通过应用迭代程序。由于迭代过程的计算复杂性,我们建议使用高级综合工具对KM算法进行硬件实现,从而有可能探索不同类型的实现,从而显着减少计算时间并减少硬件资源。

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