| Type of Material: | Thesis |
| Title: | CLASSIFICATION PROBLEMS USING INTELLIGENT ALGORITHMS |
| Researcher: | SIVAKUMAR R |
| Guide: | M.SRIDHAR |
| Department: | Department of Electronics and Communication Engineering |
| Publisher: | Bharath University, Chennai |
| Place: | Chennai |
| Year: | 2011 |
| Language: | English |
| Subject: | Defuzzification | Gene Inverse Operator | Gene Max-Min Operator | Mixed Genetic Algorithm | Electrical Engineering | Engineering and Technology |
| Dissertation/Thesis Note: | PhD; Department of Electronics and Communication Engineering, Bharath University, Chennai, Chennai; 2011; D07EC019 |
| Fulltext: | Shodhganga |
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| 008 | __ | 240913t2011||||ii#||||g|m||||||||||eng|| |
| 035 | __ | |a(IN-AhILN)th_454534 |
| 040 | __ | |aBHAU_600073|dIN-AhILN |
| 041 | __ | |aeng |
| 100 | __ | |aSIVAKUMAR R|eResearcher |
| 110 | __ | |aDepartment of Electronics and Communication Engineering|bBharath University, Chennai|dChennai|ein |
| 245 | __ | |aCLASSIFICATION PROBLEMS USING INTELLIGENT ALGORITHMS |
| 260 | __ | |aChennai|bBharath University, Chennai|c2011 |
| 300 | __ | |dDVD |
| 502 | __ | |bPhD|d2011|oD07EC019|cDepartment of Electronics and Communication Engineering, Bharath University, Chennai, Chennai |
| 518 | __ | |d2007-01-05|oDate of Registration |
| 520 | __ | |aMost of the real world problems in engineering, medicine, industry, science and business involves data classification task. Data classification takes labeled data samples and generates a classifier model that classifies new data samples into different predefined groups or classes. This thesis explores the application of Computational Intelligent Techniques like Artificial Neural Network (ANN) and Fuzzy Logic (FL) for solving data classification problems. Artificial Neural Network has emerged as an important tool for solving data classification problem. Artificial Neural Network is an information-processing paradigm inspired by the way the brain process information. It is composed of a large number of highly interconnected processing elements (neurons) working in parallel to solve a specific problem. Due to their powerful non-linear function approximation and adaptive learning capabilities ANN can make accurate data classification. Many of the data classification problems in engineering involve too many in |
| 650 | __ | |aElectrical Engineering|2UGC |
| 650 | __ | |aEngineering and Technology|2AIU |
| 653 | __ | |aDefuzzification |
| 653 | __ | |aGene Inverse Operator |
| 653 | __ | |aGene Max-Min Operator |
| 653 | __ | |aMixed Genetic Algorithm |
| 700 | __ | |aM.SRIDHAR|eGuide |
| 856 | __ | |uhttp://shodhganga.inflibnet.ac.in/handle/10603/170898|yShodhganga |
| 905 | __ | |afromsg |
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