【作者】 廖德贤；

【导师】 周新志；

【作者基本信息】 四川大学 ， 模式识别与智能系统， 2005， 硕士

【摘要】 模糊逻辑、神经网络和遗传算法是当今人工智能领域的三大核心技术。如何将它们三者有机地结合起来,互补互利,协调地应用于一个系统中,更是当前研究的热点,是很有发展前景的研究课题。 本文首先对模糊推理系统、神经网络、遗传算法这三大智能技术作了较为系统的介绍。然后,在分析了模糊系统和神经网络各自特点的基础上,探论了它们几种不同的结合方式。本文主要研究了以结构等价型结合的,基于Sugeno模糊模型的自适应神经模糊推理系统(ANFIS)的结构及推理机制,并对其学习方法进行了详细分析。针对此方法基于梯度法学习易陷入局部极值的固有缺陷,本文提出了一种综合遗传算法、BP算法和最小二乘法优势的混合学习方法。这种新混合学习方法首先利用遗传算法得到ANFIS所有参数的一个全局近似最优解,然后再利用BP算法和最小二乘法分别对前提参数和结论参数进行细化调整。因此,它一方面改善了BP算法的收敛性,使ANFIS对专家知识的依赖性大为降低,提高了系统的智能化水平;另一方面有效的提高了遗传算法的搜索效率,强化了ANFIS的学习能力。本文还提出了利用这种新方法同时实现ANFIS结构和参数优化的具体设计方案。最后,本文通过两个仿真实例验证了这种新方法的有效性,仿真结果表明,它比原来的方法有更好的ANFIS参数学习效果。更多还原

【Abstract】 Fuzzy logic, neural network, and genetic algorithm are nowadays three key technologies in the field of artificial intelligence. How to integrate these three technologies into one system and make them complementary and mutually beneficial and coordinately work in this system is even a current hot research subject and a very promising issue for study.Firstly, fuzzy inference system, neural network, and genetic algorithm that are three intelligent technologies are systematically introduced in this paper. Secondly, based on the analysis of strong and weak points of fuzzy system and neural network, several ways for their combination are discussed. And then, this paper focuses on the study of the structure and reasoning mechanism of Adaptive-Network-based Fuzzy Inference System (ANFIS), which is based on Sugeno fuzzy model and established by the way of equal-structured combination, and analyzes its learning method in detail. In view of the inherent disadvantage, high possibility of reaching local extremum, of the gradient-descent-based learning method, this paper presents a new hybrid learning method which combines the advantages of Genetic Algorithm (GA), Back Propagation (BP) algorithm and Least-Square Estimate (LSE). In this method, a global approximately optimal solution of all ANFIS parameters is obtained using genetic algorithm and then premise parameters and consequent parameters are fine tined respectively using BP algorithm and Least Squares Estimate. Therefore, this method not only improves the convergence of BP algorithm, which leads to greatly reducing the dependency of expert knowledge and enhancing the intelligent level of the system, but also improves the searching efficiency of genetic algorithm and strengthens the learning ability of ANFIS. Thispaper also presents a concrete scheme of simultaneously implementing the structure and parameters optimization of ANFIS using this new method. At last, in this paper the effectiveness of this method is verified by two simulation examples, and the simulation results show that it has better learning effects of ANFIS parameters than the former method.更多还原