Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/8539
標題: 藉由加強型連續螞蟻群聚最佳化設計前向型/遞迴模糊系統
Feedforward/Recurrent Fuzzy System Design Using Advanced Continuous Ant Colony Optimization
作者: 張博涵
Chang, Bo-Han
關鍵字: Continuous Ant Colony Optimization;連續螞蟻群聚最佳化;Feedforward;Recurrent;Fuzzy systems;前向型;遞迴型;模糊系統
出版社: 電機工程學系所
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摘要: 
本論文提出藉由分群連續螞蟻群聚最佳化(RCACO)設計前向型模糊系統,RCACO決定模糊系統的群數和最佳化每群的所有參數。它使用即時分群法決定群數並設定每群適當的初始參數,然後藉由連續螞蟻群聚最佳化每群的參數。不同於傳統的螞蟻群聚最佳化(ACO),ACO最佳化在離散域下,RCACO能夠最佳化在連續域中並且能達成較好的學習效率。在RCACO裡,螞蟻行經的路徑被當作每群的前件部和後件部的值,新的路徑挑選方式是透過初始解的費洛蒙量多寡決定,此解在高斯機率分布作取樣的動作,然後藉由區域最佳解從新定義。為了驗證RCACO的效能,模擬了三種非線性模糊控制的例子,與其他群聚智慧和基因演算法做比較。
本論文也提出藉由菁英連續螞蟻群聚最佳化(ECACO)設計遞迴型模糊系統,遞迴型模糊系統在此利用TSK-type設計簡稱TRFN。ECACO決定TRFN所有參數。不同於RCACO,ECACO藉由菁英解從新定義解。為了驗證ECACO的效能,模擬了動態例子和CSTR三個例子,與其他群聚智慧和基因演算法做比較。

This thesis proposes fuzzy system design using Rule-based Continuous Ant Colony Optimization (RCACO). RCACO determines the number of fuzzy rules and optimizes all the free parameters in each fuzzy rule. It uses an online rule-generation method to determine the number of rules and to identify suitable initial parameters for the rules, and then optimizes all free parameters using continuous ant colony optimization (ACO). In contrast to traditional ACO, which optimizes in the discrete domain, the RCACO optimizes parameters in the continuous domain and can achieve greater learning accuracy. In RCACO, the path of an ant is regarded as a combination of antecedent and consequent parameters from all rules. A new path selection method based on pheromone levels is proposed for initial solution construction. The solution is modified by sampling from a Gaussian probability density function, and it is then refined using the group best solution. Simulations on fuzzy control of three nonlinear plants are conducted to verify RCACO performance. Comparisons with other swarm intelligence and genetic algorithms demonstrate advantages of RCACO.
This thesis also proposes a recurrent fuzzy network design using Elite-guided Continuous Ant Colony Optimization (ECACO). The recurrent fuzzy network designed here is the Takagi-Sugeno-Kang (TSK)-type Recurrent Fuzzy Network (TRFN), in which each fuzzy rule contains feedback loops for handling dynamic system processing problems. ECACO optimizes all the free parameters in each recurrent fuzzy rule. In contrast to RCACO, the solution generated by continuous ACO is refined using the global elite solution with time-varying coefficients. To verify the performance of ECACO, controls of two dynamic plants and a continuous-stirred tank reactor are simulated. ECACO performance is also compared with other swarm intelligence and genetic algorithms in these simulations.
URI: http://hdl.handle.net/11455/8539
其他識別: U0005-1808200914512100
Appears in Collections:電機工程學系所

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