Fuzzy Logic, Artificial Neural Network and Genetic Algorithm over Sylvester Law of Inertia based Data Analysis Technique
By: Jain, Swati
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Contributor(s): Jain, Vikas Kumar.
Publisher: New Delhi Journals Pub 2019Edition: Vol.5(1), Jan-Jun.Description: 22-29p.Subject(s): EXTC EngineeringOnline resources: Click here
In:
International journal of embedded systems and emerging technologiesSummary: Compactness of the Fuzzy Logic (FL), Artificial Neural Network (ANN) and Genetic Algorithm (GA) is applied to analyze the data. This paper presents a foundation for the same in compact form over the Sylvester’s Law of Inertia (SLI). The sequence of FL, ANN and GA is studied for deciding the source of output data. The data interacts with the academic institutes. The academic planning is based on the existed data. Thus, its characteristic is required precisely. The proposed technique provides the layers of optimized data for the further decision. FL set the data according to the membership function. The data generalizes by the ANN. Finally, GA constructs a new decision system based on multiple layers of selection, crossover and mutation.
| Item type | Current location | Call number | Status | Date due | Barcode | Item holds |
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Articles Abstract Database
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School of Engineering & Technology Archieval Section | Not for loan | 2020125 |
Compactness of the Fuzzy Logic (FL), Artificial Neural Network (ANN) and Genetic Algorithm (GA) is applied to analyze the data. This paper presents a foundation for the same in compact form over the Sylvester’s Law of Inertia (SLI). The sequence of FL, ANN and GA is studied for deciding the source of output data. The data interacts with the academic institutes. The academic planning is based on the existed data. Thus, its characteristic is required precisely. The proposed technique provides the layers of optimized data for the further decision. FL set the data according to the membership function. The data generalizes by the ANN. Finally, GA constructs a new decision system based on multiple layers of selection, crossover and mutation.
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