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基于递阶遗传算法的模糊控制器的规则生成和参数整定 总被引:3,自引:0,他引:3
提出了一种基于递阶遗传算法的模糊控制器的优化设计方法.采用具有层次结构染色体编码方式的遗传算法来设计模糊控制器,实现了语言控制规则的自动生成和隶属函数参数的自动整定.设计过程无需系统的先验知识和训练数据,具有自组织、自学习的特点.仿真结果表明,该方法优化得到的模糊控制器结构简单、性能优良. 相似文献
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模糊控制规则优化方法研究 总被引:5,自引:1,他引:5
张景元 《计算机工程与设计》2005,26(11):2917-2919,2948
模糊控制规则的选择是模糊控制器设计的关键问题之一,在现有应用遗传算法优化模糊控制规则的方法进行研究的基础上,以模糊控制规则的完整性和一致性为出发点,提出了一种用遗传算法来优化模糊控制规则的改进算法,具体给出了遗传算法设计中的各种函数和算子的确定,并将优化过的规则用于设计模糊控制器,进行仿真研究,取得了令人满意的效果。 相似文献
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文中提出了一种基于遗传算法的生成隶属度函数的方法,该方法通过遗传算法对初始种群进行优化,获得一个适应度较高的隶属度函数编码,然后再根据机场噪声数据的实际标准对优化后得到的隶属度函数进行修正,进而得到梯形分布的隶属度函数编码.最后通过得到的隶属度函数对数据进行模糊化,并采用FP-trees算法生成模糊关联规则.该文针对数量型属性提出了这种方法,它的优点是能够使通过遗传算法得到的较优的隶属度函数更加适用于实际的数据集. 相似文献
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基于遗传算法的Fuzzy规则自动获取的研究 总被引:14,自引:1,他引:13
为了实现Fuzzy规则自动获取,进而构造高性能智能系统和解决智能系统的瓶颈问题,研究了利用遗传算法自动获取规则的方法以及遗传算法的组合优化能力.模拟结果表明,这是一种有效地获取Fuzzy规则的方法. 相似文献
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基于TS模糊神经网络的Fuzzy规则自动获取研究 总被引:1,自引:0,他引:1
Fuzzy规则的获取一直是模糊智能系统的一个瓶颈。醉在深入研究TS模糊神经网络的物理意义的基础上,给出了使用遗传算法优化模糊规则集的算法并提出了从训练后的TS模糊神经网络中抽取Fuzzy规则的可操作方法。分析和实验证明,这种方法可以实现且是有效的,对于Fuzzy规则自动获取的研究具有积极的借鉴意义。 相似文献
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模糊神经网络即具有输入信号是模糊量的神经网络,是模糊系统与神经网络相结合的产物,汇聚了二者的优点;遗传算法是一种自适应全局优化概率搜索算法。研究了基于模糊神经网络与遗传算法相融合的一种算法,在应用模糊神经网络进行数据挖掘前,应用遗传算法完成隶属函数的训练,以便更好地进行模糊神经网络学习;经过模糊神经网络学习后,提取相关规则,再次应用遗传算法,进行规则剪枝,提高数据挖掘效率。实验表明,与传统方法相比,该方法能够更快速、更加准确地进行数据挖掘,提取更精确的推理规则。 相似文献
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Fuzzy rule derivation is often difficult and time-consuming, and requires expert knowledge. This creates a common bottleneck in fuzzy system design. In order to solve this problem, many fuzzy systems that automatically generate fuzzy rules from numerical data have been proposed. In this paper, we propose a fuzzy neural network based on mutual subsethood (MSBFNN) and its fuzzy rule identification algorithms. In our approach, fuzzy rules are described by different fuzzy sets. For each fuzzy set representing a fuzzy rule, the universe of discourse is defined as the summation of weighted membership grades of input linguistic terms that associate with the given fuzzy rule. In this manner, MSBFNN fully considers the contribution of input variables to the joint firing strength of fuzzy rules. Afterwards, the proposed fuzzy neural network quantifies the impacts of fuzzy rules on the consequent parts by fuzzy connections based on mutual subsethood. Furthermore, to enhance the knowledge representation and interpretation of the rules, a linear transformation from consequent parts to output is incorporated into MSBFNN so that higher accuracy can be achieved. In the parameter identification phase, the backpropagation algorithm is employed, and proper linear transformation is also determined dynamically. To demonstrate the capability of the MSBFNN, simulations in different areas including classification, regression and time series prediction are conducted. The proposed MSBFNN shows encouraging performance when benchmarked against other models. 相似文献
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A Genetic Fuzzy System (GFS) is basically a fuzzy system augmented by a learning process based on a genetic algorithm (GA). Fuzzy systems have demonstrated their ability to solve different kinds of problems in various application domains. Currently, there is an increasing interest to augment fuzzy systems with learning and adaptation capabilities. Two of the most successful approaches to hybridize fuzzy systems with learning and adaptation methods have been made in the realm of soft computing. The GA can be merged with Fuzzy system for different purposes like rule selection, membership function optimization, rule generation, co-efficient optimization, for data classification. Here we propose an Adaptive Genetic Fuzzy System (AGFS) for optimizing rules and membership functions for medical data classification process. The primary intension of the research is 1) Generating rules from data as well as for the optimized rules selection, adapting of genetic algorithm is done and to explain the exploration problem in genetic algorithm, introduction of new operator, called systematic addition is done, 2) Proposing a simple technique for scheming of membership function and Discretization, and 3) Designing a fitness function by allowing the frequency of occurrence of the rules in the training data. Finally, to establish the efficiency of the proposed classifier the presentation of the anticipated genetic-fuzzy classifier is evaluated with quantitative, qualitative and comparative analysis. From the outcome, AGFS obtained better accuracy when compared to the existing systems. 相似文献
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模糊Petri网(Fuzzy Petri Nets, FPN)是一种适合于描述异步并发事件的计算机系统模型,可以有效地对并行和并发系统进行形式化验证和决策分析.针对聚驱综合调整系统知识具有不确定性和模糊性的特点,给出了基于加权模糊产生式规则的加权FPN决策模型.在此模型的基础上,给出了决策推理过程的形式化推理算法.算法考虑了推理过程中的众多约束条件,将复杂的推理过程采用矩阵运算来实现,充分利用了FPN的并行处理能力,使决策推理过程更加简单和快速.并以压裂方式调整为例,说明了该模型具有直观、表达能力强和易于推理等优点,具有较强的实用价值. 相似文献
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目前,自治式水下机器人(Autonomous Underwater Vehicle,AUV)、自动导引驾驶小汽车、轮船等领域应用模糊规则控制已经受到许多人的关注,模糊规则的制定与训练是其中之关键所在,该文将模朔规则控制应用在无人机自由编队飞行控制中。在训练模糊规则过程中,常规的BP神经网络法存在学习速度慢、无法结合号家知识以及容易陷入局部最小等缺点,为了克服上述不足,文中引人了补偿模糊神经网络,它足一个结合了补偿模糊逻辑和神经网络的混合系统,由面向控制和面向决策的神经元组成,其模糊运算采用动态的、全局优化运算,学习速度快、学习过程稳定。将其用于无人机自由编队飞行的模糊控制规则进行训练,结果表明用补偿模糊神经网络刘模糊规则的训练效果良好。 相似文献
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针对设计高维模糊控制器过程中会遇到的“规则爆炸”问题,利用蚁群算法进行控制规则的过滤简化。为了用尽量少的规则得到尽可能好的控制效果,利用蚁群算法在饵决组合优化问题中的强大优势,在已有的完备规则中优选出若干条规则嵌人模糊控制器。采用带有时间窗口的蚁群算法去克服遗传算法优选模糊控制规则时可能产生的规则不连续的问题。该文还从遗传算法和蚁群算法工作机制的角度分析了对这两种算法加入约束条件的可操作性。以单级倒立摆控制系统为对象进行仿真研究,最后的仿真结果表明该文方法可以使模糊控制规则具有更好的简化效果和鲁棒性,并能具有好的控制效果。 相似文献
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模糊规则类知识管理及实践 总被引:1,自引:0,他引:1
模糊规则是模糊决策系统的核心内容。该文对模糊规则的描述进行了分析,设计和开发了模糊规则的管理软件,并以控件形式与决策系统集成。详细介绍了模糊规则控件的属性和触发方法,模糊规则管理器的设计和开发,为各领域模糊分析和决策系统的开发提供了重要的工具,有利于对模糊规则类经验知识的积累和重复使用,促进模糊分析和决策工作的应用。 相似文献
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In this study, we explore the combination of two well defined topics in fuzzy systems research: fuzzy rule based systems, and information granulation. Rule based systems are a powerful and well-studied form of knowledge representation, due to their approximation abilities and interpretability. In recent years, these types of systems have become increasingly powerful with regards to modeling accuracy; however, many of these improvements come at the cost of model interpretability. This recent direction of research has left an unexplored avenue towards the generation of increasingly interpretable fuzzy rule based models, which we intend to explore. Information granulation is a relatively new, yet very promising area of research in human centric systems. As a form of knowledge representation, information granulation is very well suited to fuzzy rule based systems, where rules represent linguistic quantities in a, intuitively understandable format. It is notable that the combination of these two concepts has been left largely unstudied. We aim to explore this union by defining a methodology for the construction of a partially granular fuzzy rule based model. The aim of this novel model format is to provide a first step in the improvement of fuzzy model interpretability, through the use of information granulation. We are additionally interested in studying new ways of generating fuzzy rules; hence, we will also look at the use of hierarchical clustering as a potential alternative to the tried and tested Fuzzy C Means clustering algorithm. The models created using hierarchical clustering are then compared with those generated using Fuzzy C Means to evaluate the effectiveness of this algorithm. As a result of these experiments, we demonstrate that partially granular fuzzy rules are capable of providing a significant improvement to fuzzy rule interpretability, and we believe that granular fuzzy models present an exciting avenue of future research in human centric systems. 相似文献
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D. Nauck 《Neural computing & applications》2000,9(1):60-70
Neuro-fuzzy systems have recently gained a lot of interest in research and application. They are approaches that use learning
techniques derived from neural networks to learn fuzzy systems from data. A very simple ad hoc approach to apply a learning
algorithm to a fuzzy system is to use adaptive rule weights. In this paper, we argue that rule weights have a negative effect
on the linguistic interpretation of a fuzzy system, and thus remove one of the key advantages for applying fuzzy systems.
We show how rule weights can be equivalently replaced by modifying the fuzzy sets of a fuzzy system. If this is done, the
actual effects that rule weights have on a fuzzy rule base become visible. We demonstrate at a simple example the problems
of using rule weights. We suggest that neuro-fuzzy learning should be better implemented by algorithms that modify the fuzzy
sets directly without using rule weights. 相似文献
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