Modeling and Analysis of Mamdani Two-Term Controllers Using Non-Uniformly Distributed Multiple Fuzzy Sets and CoA/CoG Defuzzification

Praharaj, Manoranjan; Mohan, B.M.

doi:10.1080/02564602.2021.1933628

Cited by 7 publications

(2 citation statements)

References 31 publications

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“…A common and widely used method is the centroid, in which the fuzzy output is transformed into a real number that represents the center of gravity of such a fuzzy output set [38]. However, more efficient methods, such as the COA, reduce the computational load required to carry out this process.…”

Section: Defuzzificationmentioning

confidence: 99%

Fuzzy Logic and Genetic-Based Algorithm for a Servo Control System

et al. 2022

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Performing control is necessary for processes where a variable needs to be regulated. Even though conventional techniques are widely preferred for their implementation, they present limitations in systems in which the parameters vary over time, which is why methods that use artificial intelligence algorithms have been developed to improve the results given by the controller. This work focuses on implementing a position controller based on fuzzy logic in a real platform that consists of the base of a 3D printer, the direct current motor that modifies the position in this base, the power stage and the acquisition card. The contribution of this work is the use of genetic algorithms to optimize the values of the membership functions in the fuzzification of the input variables to the controller. Four scenarios were analyzed, in which the trajectory and the weight of the system were modified. The results obtained in the experimentation show that the rising and setting times of the proposed controller are better than those obtained by similar techniques that were previously developed in the literature. It was also verified that the proposed technique reached the desired values even when the initial conditions in the system changed.

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Section: Defuzzificationmentioning

confidence: 99%

Fuzzy Logic and Genetic-Based Algorithm for a Servo Control System

et al. 2022

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“…Where x represents an element of the instance and µ i (x) indicates the membership degree of the element x. The following figure 7 illustrates the representation graphic of the Weighted Average defuzzification approach Mean-Max Membership : this approach is also referred to as the medium of maxima procedure [51]. It is very closely linked to the maximum membership function, with the exception that the peak membership positions can be nonunique.…”

Section: Tweetmentioning

confidence: 99%

Emotion Processing by Applying a Fuzzy-Based Vader Lexicon and a Parallel Deep Belief Network Over Massive Data

et al. 2022

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Emotion processing has been a very intense domain of investigation in data analysis and NLP during the previous few years. Currently, the algorithms of the deep neural networks have been applied for opinion mining tasks with good results. Among various neuronal models applied for opinion mining a deep belief network (DBN) model has gained more attention. In this proposal, we have developed a combined classifier based on fuzzy Vader lexicon and a parallel deep belief network for emotion analysis. We have implemented multiple pretreatment techniques to improve the quality and soundness of the data and eliminate disturbing data. Afterward, we have performed a semi-automatic dataset labeling using a combination of two different methods: Mamdani's fuzzy system and Vader lexicon. As well, we have applied four feature extractors, which are: GloVe, TFIDF (Trigram), TFIDF (Bigram), TFIDF (Unigram) with the aim of transforming each incoming tweet into a digital value vector. In addition, we have integrated three feature selectors, namely: The ANOVA method, the chi-square approach and the mutual information technique with the objective of selecting the most relevant features. Further, we have implemented the DBN as classifier for classifying each inputted tweet into three categories: neutral, positive or negative. At the end, we have deployed our proposed approach in parallel way employing both Hadoop and Spark framework with the purpose of overcoming the problem of long runtime of massive data. Furthermore, we have carried out a comparison between our newly suggested hybrid approach and alternative hybrid models available in the literature. From the experimental findings, it was found that our suggested vague parallel approach is more powerful than the baseline patterns in terms of false negative rate (1.33%), recall (99.75%), runtime (32.95s), convergence, stability, F1 score (99.53%), accuracy (98.96%), error rate (1.04%), kappa-Static (99.1%), complexity, false positive rate (0.25%), precision rate (97.59%) and specificity rate (98.67%). As a conclusion, our vague parallel approach outperforms baseline and deep learning models, as well as certain other approaches chosen from the literature.

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A novel auto-weighting deep-learning fuzzy collaborative intelligence approach

Wang

Chen

2023

Decision Analytics Journal

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Modeling and Analysis of Mamdani Two-Term Controllers Using Non-Uniformly Distributed Multiple Fuzzy Sets and CoA/CoG Defuzzification

Cited by 7 publications

References 31 publications

Fuzzy Logic and Genetic-Based Algorithm for a Servo Control System

Fuzzy Logic and Genetic-Based Algorithm for a Servo Control System

Emotion Processing by Applying a Fuzzy-Based Vader Lexicon and a Parallel Deep Belief Network Over Massive Data

A novel auto-weighting deep-learning fuzzy collaborative intelligence approach

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