碩士 / 國立臺灣大學 / 生物環境系統工程學研究所 / 93 / The standard back propagation neural network (BPNN) uses the steepest descent method to search the optimal solution for the random initial value of connecting weights. However, the search result of this approach is highly dependent on the initial weights. It is difficult to tell whether the initial weights are close to the global minima and the searched solution could easily reach a local minimum when the weight space is complex. To solve this problem, the search process usually is run with a large number of sets of initial weights. That consumes lots of time for try-and-error and it is not an effective searching strategy.
In this study, we propose a hybrid searching strategy, combining Genetic Algorithms (GA) with the Conjugate Gradient Algorithm (CG) as the search engine of BPNN, to improve the standard searching strategy. In this hybrid strategy, GA can globally search the weight space to get a number of better candidate solutions in its iterative generations. After GA process reached a stable condition, CG is then used to optimize the weights of BPNN. This hybrid searching strategy is not only effective but also has high possibility to reach the global optima.
For demonstrating the performance of the proposed searching strategy, the urban drainage system of Zhong-Gang Catchment located in Wenshan District of Taipei City is used to evaluate its applicability and efficiency. We apply the proposed model to search the optima weights of BPNN to predict one-step-ahead and two-step-ahead sewer stage during flood events. The results show that the proposed strategy is robust and efficiency.
| Identifer | oai:union.ndltd.org:TW/093NTU05404016 |
| Date | January 2005 |
| Creators | Chien-Yao Chiu, 邱建堯 |
| Contributors | 張斐章 |
| Source Sets | National Digital Library of Theses and Dissertations in Taiwan |
| Language | zh-TW |
| Detected Language | English |
| Type | 學位論文 ; thesis |
| Format | 72 |
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