利用以群為基礎之部落演算法於函數類神經模糊系統

本論文提出了兩個演算法使用於函數類神經模糊系統。這兩個演算法分別是以群為基礎之部落最佳化演算法以及部落的粒子群最佳化演算法。本論文主要為兩大部分。在第一部分中，我們提出一個以群為基礎之部落最佳化演算法。在此演算法中，我們採用自我分群演算法將群體分成多個部落並利用不同的演化策略來更新每個個體。此外，所提出之演算法還採用一個適應機制來產生或移除粒子以及重新架構部落連結。此適應機制可以改善部落的品質及部落適應。在第二部分中，一個部落的粒子群最佳化演算法被提出為了有效的平衡搜索空間中局部以及全域探索的能力。此演算法與原來的部落演算法不同在於此演算法中的演化策略是由三種不同形式的更新公式所構成，而這些公式是以粒子群最佳化為基礎來發展並且根據每個個體的狀態所進行設計。最後，本論文提出兩種演算法使用於函數W類神經系統並應用在各種預測問題上。本論文的結果驗證了所提出演算法之有效性。

關鍵字

預測問題；太陽黑子預測；混沌訊號預測；混沌時間序列預測；類神經模糊系統；部落最佳化演算法；粒子群最佳化演算法

並列摘要

This dissertation proposes two algorithms for functional neurofuzzy systems (FNS) in predictive problems. The two algorithms are including the cluster-based tribes optimization algorithm (CTOA), and the tribal particle swarm optimization (TPSO). This dissertation consists of the two major parts. In the first part, the CTOA method is presented for the FNS model. The CTOA adopts a self-clustering algorithm (SCA) to divide a swarm into multiple tribes and uses various evolutionary strategies to update each particle. Furthermore, the CTOA also uses an adaptation mechanism to generate or remove particles and reconstruct tribal links. The adaptation mechanism can improve the qualities of the tribe and the tribe adaptation. In the second part, the TPSO method is presented to balance the local and global exploration of the search space effectively. The evolutionary strategies of TPSO have three different types of equations that are developed base on PSO according to the status of each particle to design. Finally, the proposed two algorithms for FNS model are applied in various predictive problems. Results of this dissertation demonstrate the effectiveness of the proposed algorithms.

並列關鍵字

Prediction ； sunspots ； chaotic signal ； chaotic time series ； neurofuzzy systems ； tribes optimization algorithm ； particle swarm optimization

參考文獻

[1] Y. Gao, M. J. Er, “NARMAX time series model prediction: feedforward and recurrent fuzzy neural network approaches,” Fuzzy Sets and Systems, vol. 150, no. 2, pp. 331-350, Mar. 2005.

[2] C. J. Lin, C. H. Chen, C. T. Lin, “A hybrid of cooperative particle swarm optimization and cultural algorithm for neural fuzzy networks and its prediction applications,” IEEE Trans. Syst. Man Cybern. C: Applications and Reviews, vol. 39, no. 1, pp. 55-68, Jan. 2009.

[3] C. T. Lin, C. S. G. Lee, Neural Fuzzy Systems: A Neuro-Fuzzy Synergism to Intelligent System, NJ: Prentice-Hall, 1996.

[4] N. Kasabov, Foundations of Neural Networks, Fuzzy Systems and Knowledge Engineering. Cambridge, MA: MIT Press, 1996.

[5] J. S. R. Jang, “ANFIS: Adaptive-network-based fuzzy inference system,” IEEE Trans. Syst., Man, and Cybern., vol. 23, no. 3, pp. 665-685, May/June 1993.

國際替代計量

利用以群為基礎之部落演算法於函數類神經模糊系統

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