The Method of Hardware Implementation of Fuzzy Systems on FPGA

Przybył, Andrzej; Er, Meng Joo

doi:10.1007/978-3-319-39378-0_25

Cited by 7 publications

(1 citation statement)

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“…In the same way, other nonlinear functions, such as square root, sigmoid, or gaussoid, can also be approximated. The last two nonlinear functions are used in various types of computational intelligence algorithms, such as artificial neural networks, radial function networks, or fuzzy structures [29][30][31]. Based on the LTSE solution, the approximation time for such functions (assuming a similar level of precision) is usually a dozen or so clock cycles.…”

Section: Fixed-point Coprocessor Based On a Scaling Schedulementioning

confidence: 99%

Fixed-Point Arithmetic Unit with a Scaling Mechanism for FPGA-Based Embedded Systems

Przybył¹

2021

Electronics

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The work describes the new architecture of a fixed-point arithmetic unit. It is based on the use of integer arithmetic operations for which the information about the scale of the processed numbers is contained in the binary code of the arithmetic instruction being executed. Therefore, this approach is different from the typical way of implementing fixed-point operations on standard processors. The presented solution is also significantly different from the one used in floating-point arithmetic, as the decision to determine the appropriate scale is made at the stage of compiling the code and not during its execution. As a result, the real-time processing of real numbers is simplified and, therefore, faster. The described method provides a better ratio of the processing efficiency to the complexity of the digital system than other methods. In particular, the advantage of using the described method in FPGA-based embedded control systems should be indicated. Experimental tests on an industrial servo-drive confirm the correctness of the described solution.

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Section: Fixed-point Coprocessor Based On a Scaling Schedulementioning

confidence: 99%