标题: 以函数链结为基础之类神经模糊网路及其应用
A Functional-Link-Based Neuro-Fuzzy Network and Its Applications
作者: 陈政宏
Cheng-Hung Chen
林进灯
Chin-Teng Lin
电控工程研究所
关键字: 类神经模糊网路;函数链结类神经网路;差分进化;共生进化;熵量测;非线性系统控制;Neuro-fuzzy networks;Functional link neural networks;Differential evolution;Symbiotic evolution;Entropy measure;Nonlinear system control
公开日期: 2007
摘要: 本篇论文提出一以函数链结为基础之类神经模糊网路及其相关学习演算法。此类神经模糊网路采用函数链结类神经网路当作模糊法则的后件部。此后件部是输入变数的非线性组合,它是利用函数展开的方式,能在高维度的输入空间中提供良好的非线性决策能力,因此,可使网路输出更具体且更逼近目标输出。本论文主要为三大部分。第一部份将详细介绍以函数链结为基础之类神经模糊网路及其线上学习演算法。此演算法包含架构学习及参数学习,架构学习是藉由熵的量测决定是否要增长一个新的法则,参数学习是使用倒传递演算法调整网路上的所有参数。由于倒传递演算法常常会得到局部最佳解。因此,在第二部份中,我们提出一改良式差分进化演算法,所提出的演算法与传统差分进化演算法是不同的,在于我们使用一有效的搜寻机制使得每条个体能更新在目前最佳解和乱数搜寻解之间,并采用以群为基底的突变方式以提高个体间彼此的差异性。但以上的进化演算法,无法决定该使用多少法则数。因此,在第三部份中,我们提出一以法则为基础的共生差分进化演算法。此演算法是利用多个子族群进行进化,每个子族群的个体代表每条模糊法则,且每个子族群能各自进化。此外,这演算法也能自动决定子族群数,并最佳化网路上的所有参数。最后,我们将与其他方法比较,以证实所提出的网路架构及其相关演算法之有效性。
This dissertation proposes a functional-link-based neuro-fuzzy network (FLNFN) and its related learning algorithms. The proposed FLNFN model uses a functional link neural network to the consequent part of the fuzzy rules. The consequent part uses a nonlinear functional expansion to form arbitrarily complex decision boundaries. Thus, the local properties of the consequent part in the FLNFN model enable a nonlinear combination of input variables to be approximated more effectively. This dissertation consists of three major parts. In the first part, the FLNFN model and an online learning are presented. The online learning algorithm consists of structure learning and parameter learning. The structure learning depends on the entropy measure to determine the number of fuzzy rules. The parameter learning, based on back-propagation, can adjust the shape of the membership function and the corresponding weights of the consequent part. Unfortunately, the back-propagation learning algorithm may reach the local minima very quickly. Therefore, a modified differential evolution (MODE) is presented to optimize the FLNFN parameters in the second part. The proposed MODE learning algorithm differs from the traditional differential evolution. The MODE adopts a method to effectively search between the best individual and randomly chosen individuals, and the MODE also provides a cluster-based mutation scheme, which maintains useful diversity in the population to increase the search capability. But, the aforementioned algorithm cannot determine how many rules to be used. Therefore, a rule-based symbiotic modified differential evolution (RSMODE) is proposed for the FLNFN model in the third part. The RSMODE adopts a multi-subpopulation scheme that uses each individual represents a single fuzzy rule and each individual in each subpopulation evolves separately. Furthermore, the proposed RSMODE learning algorithm can also determine the number of rule-based subpopulation and adjust the FLNFN parameters. Finally, the proposed FLNFN model and its related learning algorithms are applied in various control problems. Results of this dissertation demonstrate the effectiveness of the proposed methods.
URI: http://140.113.39.130/cdrfb3/record/nctu/#GT009312816
http://hdl.handle.net/11536/78318
显示于类别:Thesis


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