Design and Development of Fuzzy Based Complex Machine Learning Models: Two Soft Computing Based Approaches

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2022 by IJETT Journal
Volume-70 Issue-7
Year of Publication : 2022
Authors : Ramandeep Kaur, Shakti Kumar, Amar Singh
DOI : 10.14445/22315381/IJETT-V70I7P238

How to Cite?

Ramandeep Kaur, Shakti Kumar, Amar Singh, "Design and Development of Fuzzy Based Complex Machine Learning Models: Two Soft Computing Based Approaches" International Journal of Engineering Trends and Technology, vol. 70, no. 7, pp. 366-376, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I7P238

Abstract
This paper discusses designing and developing a TSK type-0 fuzzy logic-based machine learning model using two metaheuristic approaches. The optimized model evolved from the available numerical data. Two recent soft computing-based search and optimization algorithms, namely three-parent genetic algorithm (3PGA) and parallel three-parent genetic algorithm (P3PGA), have been used in the proposed approaches to deal with higher complexities and nonlinearities efficiently. The proposed approaches work in three phases. In the first phase, the proposed approaches evolve the model structure of a fuzzy system. The second phase optimizes the parameters of the fuzzy system with the help of MSE (Mean Squared Error). In the third phase, the code generation of the optimized machine learning model was done for testing purposes. The proposed approaches are tested on a rapid battery charger dataset. These approaches are compared with manually evolved machine learning approaches like KNN, ANN, Multi Regression, and SVR. The proposed approaches successfully evolved, optimized, and implemented the model into a working program. It was observed that P3PGA based approach completely outperforms other machine learning-based approaches by a wider margin. Once evolved and tested, models, can be physically realized in hardware if needed.

Keywords
Asymmetric Searchable Encryption, Blockchain, Backward and Forward privacy, Fuzzy Keyword.

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