A novel bitwise arithmetic optimization algorithm for the rule base optimization of deep neuro-fuzzy system

Talpur, Noureen; Abdulkadir, Said Jadid; Akhir, Emelia Akashah Patah; Hasan, Mohd Hilmi; Alhussian, Hitham; Abdullah, Mohd Sohaimi

doi:10.1016/j.jksuci.2023.01.020

Cited by 7 publications

(2 citation statements)

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“…Zhao et al [9] proposed the deep neural fuzzy system (DNFS) based on ANFIS, which realized the fast learning of the HFS rule base. Talpur et al [10] proposed a novel Bitwise Arithmetic Optimization Algorithm, which is implemented as a feature selection approach to solve the problem of large rule base in DNFS. Wang [11] designed a deep convolutional fuzzy system (DCFS) based on the Wang-Mendel (WM) method, which realized the application of HFS in prediction problems.…”

Section: Introductionmentioning

confidence: 99%

“…Table 1 summarized the different techniques used in modeling the HFS. [22] 2021 Vehicular Ad-Hoc network Optimization of system complexity and accuracy 13 [15] 2022 Self-organized Optimization of system complexity 14 [10] 2023 Deep neural fuzzy system Optimization of system complexity and accuracy 15 [23] 2023 Fuzzy c-means clustering Optimization of system accuracy At present, many studies have proved that it is effective to use the global search ability of heuristic algorithms such as genetic algorithm, particle swarm optimization algorithm and differential evolution to learn the antecedent and consequent parameters of rules when building a rule base of complex fuzzy system [24][25][26][27][28]. Velliangiri et al [4] used the Taylor series and elephant herding optimization algorithm to optimize the fuzzy classifier.…”

Section: Introductionmentioning

confidence: 99%

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Deep Structure Optimization for Incremental Hierarchical Fuzzy Systems Using Improved Differential Evolution Algorithm

Zhu,

Zhao

2024

CMES

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The optimization of the rule base of a fuzzy logic system (FLS) based on evolutionary algorithm has achieved notable results. However, due to the diversity of the deep structure in the hierarchical fuzzy system (HFS) and the correlation of each sub fuzzy system, the uncertainty of the HFS's deep structure increases. For the HFS, a large number of studies mainly use fixed structures, which cannot be selected automatically. To solve this problem, this paper proposes a novel approach for constructing the incremental HFS. During system design, the deep structure and the rule base of the HFS are encoded separately. Subsequently, the deep structure is adaptively mutated based on the fitness value, so as to realize the diversity of deep structures while ensuring reasonable competition among the structures. Finally, the differential evolution (DE) is used to optimize the deep structure of HFS and the parameters of antecedent and consequent simultaneously. The simulation results confirm the effectiveness of the model. Specifically, the root mean square errors in the Laser dataset and Friedman dataset are 0.0395 and 0.0725, respectively with rule counts of rules is 8 and 12, respectively. When compared to alternative methods, the results indicate that the proposed method offers improvements in accuracy and rule counts.

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Section: Introductionmentioning

confidence: 99%