Parallel Type-2 Fuzzy Logic Co-Processors for Engine Management

Lynch, Christopher; Hagras, Hani; Callaghan, Vic

doi:10.1109/fuzzy.2007.4295486

Cited by 16 publications

(6 citation statements)

References 7 publications

(5 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The possibility for multicore implementation as well as the similarity to existing interval type-2 implementations (and as such their reuse) should facilitate the deployment of zFLSs in industrial applications facing large amounts of uncertainty while requiring real-time performance (for example, through field-programmable gate-array implementation based on the interval type-2 design shown in [24]) at minimal development and implementation cost.…”

Section: E Computational Performance Analysis Of the Zflssmentioning

confidence: 99%

Toward General Type-2 Fuzzy Logic Systems Based on zSlices

Wagner

Hagras

2010

IEEE Trans. Fuzzy Syst.

Self Cite

371

215

View full text Add to dashboard Cite

Abstract-Higher order fuzzy logic systems (FLSs), such as interval type-2 FLSs, have been shown to be very well suited to deal with the high levels of uncertainties present in the majority of real-world applications. General type-2 FLSs are expected to further extend this capability. However, the immense computational complexities associated with general type-2 FLSs have, until recently, prevented their application to real-world control problems. This paper aims to address this problem by the introduction of a complete representation framework, which is referred to as zSlicesbased general type-2 fuzzy systems. The proposed approach will lead to a significant reduction in both the complexity and the computational requirements for general type-2 FLSs, while it offers the capability to represent complex general type-2 fuzzy sets. As a proof-of-concept application, we have implemented a zSlicesbased general type-2 FLS for a two-wheeled mobile robot, which operates in a real-world outdoor environment. We have evaluated the computational performance of the zSlices-based general type-2 FLS, which is suitable for multiprocessor execution. Finally, we have compared the performance of the zSlices-based general type-2 FLS against type-1 and interval type-2 FLSs, and a series of results is presented which is related to the different levels of uncertainty handled by the different types of FLSs.Index Terms-General type-2 fuzzy logic systems (FLSs), type-2 FLSs, zSlices.

show abstract

Section: E Computational Performance Analysis Of the Zflssmentioning

confidence: 99%

Toward General Type-2 Fuzzy Logic Systems Based on zSlices

Wagner

Hagras

2010

IEEE Trans. Fuzzy Syst.

Self Cite

371

215

View full text Add to dashboard Cite

show abstract

“…Unlike existing approaches that have used Mamdani model structure, we use the Takagi-SugenoKang (TSK) for implementing the controller. Lycnh et al have successfully implemented both K-M and W-M type reduction using Mamdani model stucture and found W-M to using on average 45% less clock cycles then K-M [17]. Thus, we propose the use of a new closed-form inference engine introduced in [2], called BMM inference engine, because of its simplicity and closed-form structure.…”

Section: Introductionmentioning

confidence: 93%

Hardware implementation of a novel inference engine for interval type-2 fuzzy control on FPGA

Schrieber

Biglarbegian

2014

2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

View full text Add to dashboard Cite

Interval type-2 fuzzy logic controllers (IT2 FLCs) have shown a promising potential in handling uncertainties compared to their type-1 counterparts, and as a result, we have witnessed increasing usage of IT2 FLCs in various applications. Due to the complex structures of IT2 FLCs, using them in real-time applications might be computationally expensive. To facilitate real-time implementation of these controllers, hardware with parallel processing abilities are recommended; fieldprogrammable gate arrays (FPGA) are one class of such hardware. In this paper, we propose a structure for implementing a new IT2 FLC inference mechanism called BMM [2] -that has been recently introduced in the literature -on an FPGA. We first demonstrated how the proposed structure can be implemented on software; next, we proposed an implementation architecture for the IT2 FLC mechanism on hardware. We performed simulations and experiments on two different plants and compared the speed of our controllers. The performance speed as well as the tracking of our proposed control structure in simulations and experiments were shown to be very close to each other. Using the BMM engine for the proposed hardware structure proves to be faster than other existing controllers in the literature. Thus, it is expected that IT2 FLCs can be easily implemented on hardware to further enable their real-time applications.

show abstract

“…Since type-2 fuzzy logic controllers have shown that they can handle uncertainties and give a superior performance to the existing commercial controllers, Lynch et al [39] explored the use of parallel hardware implementations of interval type-2 fuzzy logic controllers as a means to eradicate barriers.…”

Section: Applications In Manufacturing Operations and Industriesmentioning

confidence: 99%