Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/8543
標題: 以階層多群粒子群聚最佳化設計模糊系統與類神經網路及六足機器人控制應用
Hierarchical Multi-Species PSO For Fuzzy System / Recurrent NN With Hexapod Robot Control Application
作者: 蕭哲孟
Hsiao, Che-Meng
關鍵字: PSO
粒子群聚最佳化
Fuzzy System
Hexapod Robot
模糊系統
六足機器人
出版社: 電機工程學系所
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摘要: 本論文提出一個以階層分群多群的粒子群聚演算法(HCMSPSO)來設計優化模糊系統。在HCMSPSO裡面,一大群會由分群分出很多個群組在高階層的部份,然後每一個群組會在分成很多個次群組在低階層的部份。對於一個模糊系統而言包含了r條模糊規則,也就是r個群組會形成在高階層的部份裡。在這此演算法裡,一個群組只最佳化一條單一的模糊規則,然而這r個群組會互相合作而最佳化整個模糊系統。在HCMSPSO裡面,一開始是沒有群組的。此地方用即時分群演算法去自動產生一個新的群組(模糊規則) ,而且決定每一群組適當的初始粒子位置。粒子在不同的群組代表著不同條的模糊規則,然後會獨自去做優化的動作在低階層的部份。對每一個群組而言,次群組會在每一次疊帶形成藉由粒子的分群。對於每一個次群組裡執行效果最好的粒子,會在更新粒子群的時候,被當成鄰近的最佳解。此篇論文會在模擬包含了模糊控制器,模組,和預測的問題裡證實HCMSPSO的效果。會和其他改進過的PSO演算法比較證實HCMSPSO的效果。 本論文也提出以階層多群的粒子群聚演算法(HMSPSO)來設計優化類神經網路。在HMSPSO裡面群組的數目代表著類神經網路裡節點的數目,而且事前就設定好的。跟HCMSPSO一樣,次群組的形成是根據每回合裡的群組的粒子,去分群產生的。設計單一跟多樣的類神經網路是被引導的,後者的網路架構是要用應用在六足機器人的移動控制上的問題。首先,一個多樣的類神經網路控制器是被設計用在模擬六足機器人。然後再把設計出來的控制器成功的應用在實際的六足機器人上面。
This thesis proposes a Hierarchical Clustering-based Multi-Species Particle Swarm Optimization (HCMSPSO) algorithm, called HCMSPSO, for fuzzy system optimization. A swarm in HCMSPSO is clustered into multiple species in an upper hierarchical level, and each species is further clustered into multiple subspecies in a lower hierarchical level. For a fuzzy system consisting of rules, swarms (species) are formed in the upper level. One species only optimizes a single fuzzy rule, and the species cooperatively optimize a whole fuzzy system. There are initially no species in HCMSPSO. An on-line clustering-based algorithm is proposed to generate new species (fuzzy rules) automatically and determine proper initial particle positions for each species. Particles within different species represent different rules and are optimized independently in the lower level. For each species, subspecies are formed adaptively in each iteration according to clustering of particles. The best-performing particle in each subspecies serves as the neighborhood best when updating all particles within the same subspecies. Simulations on fuzzy control, modeling, and prediction problems are conducted to verify HCMSPSO performance. Comparisons with other advanced PSO optimization algorithms demonstrate HCMSPSO performance. This thesis also proposes a Hierarchical Multi-Species Particle Swarm Optimization (HMSPSO) algorithm, called HMSPSO, for fully connected recurrent neural network (FCRNN) optimization. In HMSPSO, the number of species is equal to the number of nodes in an FCRNN and is assigned in advance. Each species only optimizes the free parameters in a node. Like HCMSPSO, subspecies are formed adaptively in each iteration according to clustering of particles in each species. Design of a single and multiple FCRNNs are conducted, where the latter network structure is applied to hexapod robot locomotion control problem. At first, a multiple-FCRNN controller is designed using a simulated hexapod robot. The designed controller is then successfully applied to a real hexapod robot control.
URI: http://hdl.handle.net/11455/8543
其他識別: U0005-1808200916415400
文章連結: http://www.airitilibrary.com/Publication/alDetailedMesh1?DocID=U0005-1808200916415400
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