Similarity and wavelet transform based data partitioning and parameter learning for fuzzy neural network
Function approximation is an important task in many different fields like economics, engineering, computing, classification, and forecasting. From a finite data set, the basic task of a function approximation method is to find a suitable relationship between variables and their corresponding respons...
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| Format: | Article |
| Language: | English |
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Springer
2022-06-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1319157820303918 |
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| author | Pramoda Patro Krishna Kumar G. Suresh Kumar Gandharba Swain |
| author_facet | Pramoda Patro Krishna Kumar G. Suresh Kumar Gandharba Swain |
| author_sort | Pramoda Patro |
| collection | DOAJ |
| description | Function approximation is an important task in many different fields like economics, engineering, computing, classification, and forecasting. From a finite data set, the basic task of a function approximation method is to find a suitable relationship between variables and their corresponding responses. In the recent past, the improved neural networks including intuitive, interpretable correlated-contours fuzzy rules for classification tasks were proposed. However, the acquired data set can contain large volume of data and noise that degrades the classification ability of the model and increases the computational time. Thus, it is important to consider this problem which was not focused on recent existing works. Furthermore, there are also some neuron regularization issues in the second layer. To solve this issue in this proposed system Bat optimization based feature selection is proposed for optimal selection of features from the available dataset. Then classification is done by using enhanced neural network including intuitive and interpretable correlated-contours fuzzy rules (EC-FR). According to fuzzy rules extraction, an appropriate framework is built-in which similarity-based directional component of data partitioning and also a model to form cloud data is presented. Neurons weight and bias values are computed by adapting wavelet functions. Finally, parameters of the fuzzy neural networks are fine-tuned using the hybrid ant colony particle swarm optimization (HASO). Performance is evaluated primarily in accordance with the subsequent metrics like precision, recall, accuracy, and error rate. |
| format | Article |
| id | doaj-art-0a481bb2fbc5443f9deb9ec994dc2253 |
| institution | Kabale University |
| issn | 1319-1578 |
| language | English |
| publishDate | 2022-06-01 |
| publisher | Springer |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj-art-0a481bb2fbc5443f9deb9ec994dc22532025-08-20T03:48:35ZengSpringerJournal of King Saud University: Computer and Information Sciences1319-15782022-06-013463424343210.1016/j.jksuci.2020.06.003Similarity and wavelet transform based data partitioning and parameter learning for fuzzy neural networkPramoda Patro0Krishna Kumar1G. Suresh Kumar2Gandharba Swain3Department of Mathematics, Koneru Lakshmaiah Education Foundation, Vaddeswaram 522502, Guntur, Andhra Pradesh, India; Corresponding author at: Department Of Mathematics, Koneru Lakshmaiah Education Foundation, Vaddeswaram 522502, Guntur, Andhra Pradesh, India.Department of Mathematics, MIT School of Engineering, MIT Art Design and Technology University, Loni Kalbhor, 412201, Pune, IndiaDepartment of Mathematics, Koneru Lakshmaiah Education Foundation Vaddeswaram 522502, Guntur, Andhra Pradesh, IndiaDepartment of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram 522502, Guntur, Andhra Pradesh, IndiaFunction approximation is an important task in many different fields like economics, engineering, computing, classification, and forecasting. From a finite data set, the basic task of a function approximation method is to find a suitable relationship between variables and their corresponding responses. In the recent past, the improved neural networks including intuitive, interpretable correlated-contours fuzzy rules for classification tasks were proposed. However, the acquired data set can contain large volume of data and noise that degrades the classification ability of the model and increases the computational time. Thus, it is important to consider this problem which was not focused on recent existing works. Furthermore, there are also some neuron regularization issues in the second layer. To solve this issue in this proposed system Bat optimization based feature selection is proposed for optimal selection of features from the available dataset. Then classification is done by using enhanced neural network including intuitive and interpretable correlated-contours fuzzy rules (EC-FR). According to fuzzy rules extraction, an appropriate framework is built-in which similarity-based directional component of data partitioning and also a model to form cloud data is presented. Neurons weight and bias values are computed by adapting wavelet functions. Finally, parameters of the fuzzy neural networks are fine-tuned using the hybrid ant colony particle swarm optimization (HASO). Performance is evaluated primarily in accordance with the subsequent metrics like precision, recall, accuracy, and error rate.http://www.sciencedirect.com/science/article/pii/S1319157820303918ClassificationFuzzy rulesNeural networkFeature selectionBat optimization |
| spellingShingle | Pramoda Patro Krishna Kumar G. Suresh Kumar Gandharba Swain Similarity and wavelet transform based data partitioning and parameter learning for fuzzy neural network Journal of King Saud University: Computer and Information Sciences Classification Fuzzy rules Neural network Feature selection Bat optimization |
| title | Similarity and wavelet transform based data partitioning and parameter learning for fuzzy neural network |
| title_full | Similarity and wavelet transform based data partitioning and parameter learning for fuzzy neural network |
| title_fullStr | Similarity and wavelet transform based data partitioning and parameter learning for fuzzy neural network |
| title_full_unstemmed | Similarity and wavelet transform based data partitioning and parameter learning for fuzzy neural network |
| title_short | Similarity and wavelet transform based data partitioning and parameter learning for fuzzy neural network |
| title_sort | similarity and wavelet transform based data partitioning and parameter learning for fuzzy neural network |
| topic | Classification Fuzzy rules Neural network Feature selection Bat optimization |
| url | http://www.sciencedirect.com/science/article/pii/S1319157820303918 |
| work_keys_str_mv | AT pramodapatro similarityandwavelettransformbaseddatapartitioningandparameterlearningforfuzzyneuralnetwork AT krishnakumar similarityandwavelettransformbaseddatapartitioningandparameterlearningforfuzzyneuralnetwork AT gsureshkumar similarityandwavelettransformbaseddatapartitioningandparameterlearningforfuzzyneuralnetwork AT gandharbaswain similarityandwavelettransformbaseddatapartitioningandparameterlearningforfuzzyneuralnetwork |