Foreign exchange prediction and trading using deep belief neural network
This project would provide an analysis on the deep belief network (DBN). A DBN would be constructed by stacking layers of restricted Boltzmann machines (RBM), and its learning process will be optimized by various optimization methods. Differing number of inputs, hidden layer and its number of neuron...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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2018
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| Online Access: | http://hdl.handle.net/10356/76355 |
| _version_ | 1826117744042442752 |
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| author | Muhammad Bin Mustaffa |
| author2 | Wang Lipo |
| author_facet | Wang Lipo Muhammad Bin Mustaffa |
| author_sort | Muhammad Bin Mustaffa |
| collection | NTU |
| description | This project would provide an analysis on the deep belief network (DBN). A DBN would be constructed by stacking layers of restricted Boltzmann machines (RBM), and its learning process will be optimized by various optimization methods. Differing number of inputs, hidden layer and its number of neurons would also be implemented. A single exchange rate would be tested against a time period while three criteria would be considered to determine its performance. All this would be achieved by using a programming software called MATLAB. |
| first_indexed | 2024-10-01T04:32:20Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/76355 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:32:20Z |
| publishDate | 2018 |
| record_format | dspace |
| spelling | ntu-10356/763552023-07-07T16:16:35Z Foreign exchange prediction and trading using deep belief neural network Muhammad Bin Mustaffa Wang Lipo School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems This project would provide an analysis on the deep belief network (DBN). A DBN would be constructed by stacking layers of restricted Boltzmann machines (RBM), and its learning process will be optimized by various optimization methods. Differing number of inputs, hidden layer and its number of neurons would also be implemented. A single exchange rate would be tested against a time period while three criteria would be considered to determine its performance. All this would be achieved by using a programming software called MATLAB. Bachelor of Engineering (Electrical and Electronic Engineering) 2018-12-20T02:35:25Z 2018-12-20T02:35:25Z 2018 Final Year Project (FYP) http://hdl.handle.net/10356/76355 en Nanyang Technological University 38 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems Muhammad Bin Mustaffa Foreign exchange prediction and trading using deep belief neural network |
| title | Foreign exchange prediction and trading using deep belief neural network |
| title_full | Foreign exchange prediction and trading using deep belief neural network |
| title_fullStr | Foreign exchange prediction and trading using deep belief neural network |
| title_full_unstemmed | Foreign exchange prediction and trading using deep belief neural network |
| title_short | Foreign exchange prediction and trading using deep belief neural network |
| title_sort | foreign exchange prediction and trading using deep belief neural network |
| topic | DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems |
| url | http://hdl.handle.net/10356/76355 |
| work_keys_str_mv | AT muhammadbinmustaffa foreignexchangepredictionandtradingusingdeepbeliefneuralnetwork |