Study of Signal Processing and Artificial Neural Networks on Prediction
| Autor: | Wei-Chen Pan, 潘偉誠 |
|---|---|
| Rok vydání: | 2013 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 101 Artificial Neural Networks are predictive algorithms which use artificial neurons to emulate biological nervous system, and it can provide parallel computing for large scale data. Neural networks are originally used and developed for biological vision and auditory systems, so it has excellent performance in speech and image recognition. Because neural networks can be implemented on hardware and it has the ability of parallel computing and generation, so neural networks can provide high performance in data prediction and classification for other domain. Signal Processing is widely used in analyzing image, video and speech. Linear Predictive Coding is an algorithm of linear predictive model in time domain. It can convert continuous signals to a linear combination equation, which is used in prediction or signals compression by fetching the Linear Predictive Coefficients of input signal. In this paper, the authors propose a mechanism that uses Linear Predictive Coding (LPC) to fetch the feature values of multi-source input signals, and then use neural networks to deal with the feature values. To emulate natural signals, the source signals are made and combined with well-known waves, such as Sine, Cosine, and triangle wave. The simulation results show that the proposed mechanism can provide 100% accuracy of correct prediction on highest similarity, which proves that proposed mechanism can provide great accuracy in prediction. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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