Modelling of All-optical 3-inputs XOR logical gates using artificial neural networks
All-optical logic gates are the most important unit for achieving all-optical processing systems. Developing a fast and efficient method for studying the behavior of all-optical logic gates is very important and has been considered by researchers. In this paper, general regression neural networks an...
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| Format: | Article |
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Semnan University
2022-09-01
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| Series: | مجله مدل سازی در مهندسی |
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| Online Access: | https://modelling.semnan.ac.ir/article_6631_0d6104ecd5cca135cd70449436ebe7db.pdf |
| _version_ | 1797296478292541440 |
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| author | samaneh hamedi hamed Dehdashti Jahromi |
| author_facet | samaneh hamedi hamed Dehdashti Jahromi |
| author_sort | samaneh hamedi |
| collection | DOAJ |
| description | All-optical logic gates are the most important unit for achieving all-optical processing systems. Developing a fast and efficient method for studying the behavior of all-optical logic gates is very important and has been considered by researchers. In this paper, general regression neural networks and linear method are used to predict a three-input all-optical XOR logic gate output. The simulation results show that both methods can precisely model the behavior of the device. The training time of the neural network in the linear method with the optimal structure is about 93 seconds, which is much longer than the GRNN method with a training time of 8 seconds. Both models predict the output in less than 1 second which show a great improvement over the conventional method with 12 seconds. In the GRNN method with the smoothing factor of 0.001, the best results were obtained with MSE, RSE and MAE error values of 1.97×10-7, 5.95×10-6, and 1.6×10-4, respectively. In the linear method with 200 initial training data, the minimum values of MSE, RSE, and MAE are 1.11×10-22, 2.14×10-16 and 2.11×10-11, respectively, and the best modeled output is achieved. The value of correlation coefficient (R2) between the modeled output and the desired output of the logic gate is one for both neural network methods, which indicates a very good prediction for this method. |
| first_indexed | 2024-03-07T22:05:20Z |
| format | Article |
| id | doaj.art-7eee41f537e24b6881b58e7491d8b5fb |
| institution | Directory Open Access Journal |
| issn | 2008-4854 2783-2538 |
| language | fas |
| last_indexed | 2024-03-07T22:05:20Z |
| publishDate | 2022-09-01 |
| publisher | Semnan University |
| record_format | Article |
| series | مجله مدل سازی در مهندسی |
| spelling | doaj.art-7eee41f537e24b6881b58e7491d8b5fb2024-02-23T19:09:41ZfasSemnan Universityمجله مدل سازی در مهندسی2008-48542783-25382022-09-01207014715910.22075/jme.2022.24374.21376631Modelling of All-optical 3-inputs XOR logical gates using artificial neural networkssamaneh hamedi0hamed Dehdashti Jahromi1Department of Electrical and electronics engineeringFaculty of Engineering, Jahrom University, Jahrom,All-optical logic gates are the most important unit for achieving all-optical processing systems. Developing a fast and efficient method for studying the behavior of all-optical logic gates is very important and has been considered by researchers. In this paper, general regression neural networks and linear method are used to predict a three-input all-optical XOR logic gate output. The simulation results show that both methods can precisely model the behavior of the device. The training time of the neural network in the linear method with the optimal structure is about 93 seconds, which is much longer than the GRNN method with a training time of 8 seconds. Both models predict the output in less than 1 second which show a great improvement over the conventional method with 12 seconds. In the GRNN method with the smoothing factor of 0.001, the best results were obtained with MSE, RSE and MAE error values of 1.97×10-7, 5.95×10-6, and 1.6×10-4, respectively. In the linear method with 200 initial training data, the minimum values of MSE, RSE, and MAE are 1.11×10-22, 2.14×10-16 and 2.11×10-11, respectively, and the best modeled output is achieved. The value of correlation coefficient (R2) between the modeled output and the desired output of the logic gate is one for both neural network methods, which indicates a very good prediction for this method.https://modelling.semnan.ac.ir/article_6631_0d6104ecd5cca135cd70449436ebe7db.pdfneural networkslinear predictiongeneralized regression neural networkall optical xor gate |
| spellingShingle | samaneh hamedi hamed Dehdashti Jahromi Modelling of All-optical 3-inputs XOR logical gates using artificial neural networks مجله مدل سازی در مهندسی neural networks linear prediction generalized regression neural network all optical xor gate |
| title | Modelling of All-optical 3-inputs XOR logical gates using artificial neural networks |
| title_full | Modelling of All-optical 3-inputs XOR logical gates using artificial neural networks |
| title_fullStr | Modelling of All-optical 3-inputs XOR logical gates using artificial neural networks |
| title_full_unstemmed | Modelling of All-optical 3-inputs XOR logical gates using artificial neural networks |
| title_short | Modelling of All-optical 3-inputs XOR logical gates using artificial neural networks |
| title_sort | modelling of all optical 3 inputs xor logical gates using artificial neural networks |
| topic | neural networks linear prediction generalized regression neural network all optical xor gate |
| url | https://modelling.semnan.ac.ir/article_6631_0d6104ecd5cca135cd70449436ebe7db.pdf |
| work_keys_str_mv | AT samanehhamedi modellingofalloptical3inputsxorlogicalgatesusingartificialneuralnetworks AT hameddehdashtijahromi modellingofalloptical3inputsxorlogicalgatesusingartificialneuralnetworks |