The Study of Predictive Models Based on the Optimal General Regression Neural Networks

• Main Authors: Shih-Chun Shao, 邵時俊
• Other Authors: 陳文輝
• Format: Others
• Language: zh-TW
• Published: 2011
• Online Access: http://ndltd.ncl.edu.tw/handle/b59efn

碩士 === 國立臺北科技大學 === 自動化科技研究所 === 99 === In engineering applications, the predictive models are always adopted to solve the actual problems. Therefore, the aim of this thesis is to study how to build up a high accuracy predictive model according to the historical data in engineering applications. Hence, general regression neural networks are applied as the core algorithm of predictive models in this thesis. It is because after we choose the spread constant, the features of the whole general regression neural networks can be determined. Therefore, it has a higher-speed learning ability than other neural networks. Also, it achieves the high accuracy of prediction with few historical sample data. Finally, this thesis adopts cross-validation method, genetic algorithms as well as particle swarm optimization to find out the best spread constant to build up three different predictive models so as to make sure they have the best prediction inferences in this thesis. First, this thesis will examine and analyze the prediction inferences of these three predictive models. Then, they will be applied to two different cases according to their features. One is the application of the data pre-processing of remote terminal units, and the other is the H.264/AVC error concealment in video communication. In the first case, this thesis will apply the proposed predictive models compared with fuzzy algorithms and back-propagation neural networks to conduct the analyzing comparison of error calibrating. In the second case, the algorithm of spatial error concealment will be added into the predictive model to improve the shortcomings of merely adopting traditional temporal error concealment. According to the experimental results of two cases, the predictive models in this thesis can solve the problems effectively no matter which type of data is imported. Furthermore, they also have a high valuation in engineering applications.