基于遗传算法的模糊控制器的优化设计——采用模糊数据挖掘技术

论文为模糊系统建模提出了一种新颖的方法——由输入输出数据集合设计基于遗传算法的模糊控制器,该方法采用模糊数据挖掘技术,从大量的输入输出数据集合中自动地提取模糊规则模型,确定模糊分割点及各变量的隶属度函数;并利用实数编码的遗传算法RGA对隶属度函数参数进行全面优化。最后通过实例及仿真验证了该方法的有效性。     

基于多目标分层遗传模糊建模的磨矿过程溢流粒度软测量

提出一种基于多目标分层遗传算法的模糊系统对溢流粒度进行软测量, 该方法将模糊系统分为4 层, 即输入层、隶属度层、规则库层和系统集成层. 为了达到各层共同进化的目的, 设计遗传算法各层编码策略, 构建基于平均绝对百分误差和均方根误差的优化目标函数, 并采用该函数计算各层个体的适应度. 鉴于模糊模型训练过程中可能出现异常解, 将L-M 贝叶斯正则化方法融入训练过程. 对磨矿生产数据的仿真实验验证了所提出方法的有效性.

     

改进的粒子群模糊聚类方法

针对基于粒子群的模糊聚类算法以隶属度编码时对噪音敏感,以及处理样本数小于样本维数的数据集效果较差等问题,通过改进其中的模糊聚类约束方法,提出一种改进的基于粒子群的模糊聚类方法.当样本对各类的隶属度之和不为1时,新方法在粒子群优化得出的隶属度基础上,根据样本与各类之间的距离对隶属度进一步分配,以使隶属度满足模糊聚类约束条件.新方法显著地改善了在隶属度编码下使用粒子群进行模糊聚类的效果,并通过典型的数据集进行了验证.     

一种优化模糊划分的遗传方法

模糊隶属函数是设计模糊系统的一个关键概念.如何生成适当的隶属函数已成为30年来一个富有挑战性的问题.利用遗传算法和信息熵来解决模糊区域划分和优化,用信息熵来作为适应度评价函数的标准.最后,利用Matlab对所给算法进行了实验,得到一个优化的模糊区域划分.     

改进遗传算法优化的矿井局部通风机模糊PID控制器设计

目前局部通风机控制主要采用传统PID控制器,控制器的参数固定且多依据经验选取,很难实现隶属度函数和模糊规则的最优组合,使得控制器难以满足局部通风机自适应的控制要求。针对该问题,设计了一种改进遗传算法优化的矿井局部通风机模糊PID控制器。在改进遗传算法中引入欧氏距离提高种群的多样性,同时引入自适应交叉和变异概率来提高算法的收敛性。在编码过程中采用比例分数间接实现对隶属度函数的优化,使得改进遗传算法能够同时优化隶属度函数和模糊规则。将优化得到的隶属度函数和模糊规则导入模糊PID控制器中,控制器能够根据局部通风机的不同工作状态,通过变频器自适应调整局部通风机的风量,实现对局部通风机的动态调节。仿真结果表明,相较于传统模糊PID控制器,改进模糊PID控制器可基本实现无超调,且上升时间缩短了56.25%,稳定时间缩短了47.06%,能够更好地满足矿井局部通风机的控制要求。     

基于遗传算法的石膏纤维板厚度模糊控制器的优化及其仿真

模糊控制中隶属度函数的正确选择是模糊控制器设计的关键,它决定了模糊控制系统的动态、静态性能和控制效果.本文针对传统的获取隶属度函数方法的不足,采用遗传算法对其进行优化,从而使经过优化所获得的隶属度函数更加合理,并将其应用在石膏纤维板厚度控制中.最后经过仿真比较研究,结果表明优化后的模糊控制器的控制品质具有较大的改善和提高.     

基于模糊数据挖掘与遗传算法的异常检测方法

建立合适的隶属度函数是入侵检测中应用模糊数据挖掘所面临的一个难点。针对这一问题，提出了在异常检测中运用遗传算法对隶属度函数的参数进行优化的方法。将隶属度函数的参数组合成有序的参数集并编码为遗传个体，在个体的遗传进化中嵌入模糊数据挖掘，可以搜索到最佳的参数集。采用这一参数集，能够在实时检测中最大限度地将系统正常状态与异常状态区分开来，提高异常检测的准确性。最后，对网络流量的异常检测实验验证了这一方法的可行性。     

基于遗传算法和语言算子的模糊控制器优化研究

用单片机实现模糊控制策略是一种常用的重要的方法,它是根据误差和误差的变化率隶属度函数表,离线计算得到一张模糊控制查询表,用单片机实现查询功能。该方法应用十分广泛,因此优化离散形式的隶属度函数表具有重要的意义。该文提出了一种优化离散形式的隶属度函数表的新方法:即用遗传算法优化模糊集合中的语气算子H,从而优化离散形式隶属函数表。经优化后的隶属函数更能客观地反映控制对象真实特性,从而达到了优化模糊控制器的目的。文章用一个具体的实例以仿真的形式验证了该方法是正确的、有效的。     

基于遗传算法的多边形分割AUV全局路径规划

针对遗传算法在实际AUV全局路径规划应用中出现运算数据大、路径规划有尖峰等问题,提出了新型路径规划方法.利用平面直角坐标系实现环境的建模,将障碍物简化成多边形并分割为三角形.路径用首尾相接的线段表示,通过固定横坐标,随机生成纵坐标的方式实现遗传算法二进制编码,对障碍物三角形交叉判断,路径距离运算实现适用度函数编写.对遗传之后的路径通过避障、删除节点、平滑的操作确定最终优化路径.结果表明,对障碍物的三角形简化实现了在遗传操作中的程序优化,利用避障、删除多余节点、平滑操作实可很好的消除尖峰,可寻找一条相对较优的路径.     

一种基于遗传算法的模糊中值滤波器优化方法

根据遗传算法可以搜索全局最优的特点,提出了一种基于遗传算法优化模糊隶属函数,从而对带有脉冲噪声的图像进行模糊中值滤波的方法。虽然模糊逻辑可以很好用于图像处理,但它的隶属函数很难准确的选取,通过遗传算法对已知样本图像进行学习,找到最优的隶属函数,然后用该隶属函数对需要处理的噪声图像进行滤波。实验表明,提出的方法可以很好地滤除图像中的脉冲噪声,自适应强。     

Genetic-Fuzzy Data Mining With Divide-and-Conquer Strategy

Data mining is most commonly used in attempts to induce association rules from transaction data. Most previous studies focused on binary-valued transaction data. Transaction data in real-world applications, however, usually consist of quantitative values. This paper, thus, proposes a fuzzy data-mining algorithm for extracting both association rules and membership functions from quantitative transactions. A genetic algorithm (GA)-based framework for finding membership functions suitable for mining problems is proposed. The fitness of each set of membership functions is evaluated by the fuzzy-supports of the linguistic terms in the large 1-itemsets and by the suitability of the derived membership functions. The evaluation by the fuzzy supports of large 1-itemsets is much faster than that when considering all itemsets or interesting association rules. It can also help divide-and-conquer the derivation process of the membership functions for different items. The proposed GA framework, thus, maintains multiple populations, each for one item's membership functions. The final best sets of membership functions in all the populations are then gathered together to be used for mining fuzzy association rules. Experiments are conducted to analyze different fitness functions and set different fitness functions and setting different supports and confidences. Experiments are also conducted to compare the proposed algorithm, the one with uniform fuzzy partition, and the existing one without divide-and-conquer, with results validating the performance of the proposed algorithm.     

An improved approach to find membership functions and multiple minimum supports in fuzzy data mining

Fuzzy mining approaches have recently been discussed for deriving fuzzy knowledge. Since items may have their own characteristics, different minimum supports and membership functions may be specified for different items. In the past, we proposed a genetic-fuzzy data-mining algorithm for extracting minimum supports and membership functions for items from quantitative transactions. In that paper, minimum supports and membership functions of all items are encoded in a chromosome such that it may be not easy to converge. In this paper, an enhanced approach is proposed, which processes the items in a divide-and-conquer strategy. The approach is called divide-and-conquer genetic-fuzzy mining algorithm for items with Multiple Minimum Supports (DGFMMS), and is designed for finding minimum supports, membership functions, and fuzzy association rules. Possible solutions are evaluated by their requirement satisfaction divided by their suitability of derived membership functions. The proposed GA framework maintains multiple populations, each for one item’s minimum support and membership functions. The final best minimum supports and membership functions in all the populations are then gathered together to be used for mining fuzzy association rules. Experimental results also show the effectiveness of the proposed approach.     

A continuous ant colony system framework for fuzzy data mining

The goal of data mining is to find out interesting and meaningful patterns from large databases. In some real applications, many data are quantitative and linguistic. Fuzzy data mining was thus proposed to discover fuzzy knowledge from this kind of data. In the past, two mining algorithms based on the ant colony systems were proposed to find suitable membership functions for fuzzy association rules. They transformed the problem into a multi-stage graph, with each route representing a possible set of membership functions, and then, used the any colony system to solve it. They, however, searched for solutions in a discrete solution space in which the end points of membership functions could be adjusted only in a discrete way. The paper, thus, extends the original approaches to continuous search space, and a fuzzy mining algorithm based on the continuous ant approach is proposed. The end points of the membership functions may be moved in the continuous real-number space. The encoding representation and the operators are also designed for being suitable in the continuous space, such that the actual global optimal solution is contained in the search space. Besides, the proposed approach does not have fixed edges and nodes in the search process. It can dynamically produce search edges according to the distribution functions of pheromones in the solution space. Thus, it can get a better nearly global optimal solution than the previous two ant-based fuzzy mining approaches. The experimental results show the good performance of the proposed approach as well.     

A fast recursive algorithm based on fuzzy 2-partition entropy approach for threshold selection

The fuzzy c-partition entropy approach for threshold selection is an effective approach for image segmentation. The approach models the image with a fuzzy c-partition, which is obtained using parameterized membership functions. The ideal threshold is determined by searching an optimal parameter combination of the membership functions such that the entropy of the fuzzy c-partition is maximized. It involves large computation when the number of parameters needed to determine the membership function increases. In this paper, a recursive algorithm is proposed for fuzzy 2-partition entropy method, where the membership function is selected as S-function and Z-function with three parameters. The proposed recursive algorithm eliminates many repeated computations, thereby reducing the computation complexity significantly. The proposed method is tested using several real images, and its processing time is compared with those of basic exhaustive algorithm, genetic algorithm (GA), particle swarm optimization (PSO), ant colony optimization (ACO) and simulated annealing (SA). Experimental results show that the proposed method is more effective than basic exhaustive search algorithm, GA, PSO, ACO and SA.     

A multi-level ant-colony mining algorithm for membership functions

Fuzzy data mining is used to extract fuzzy knowledge from linguistic or quantitative data. It is an extension of traditional data mining and the derived knowledge is relatively meaningful to human beings. In the past, we proposed a mining algorithm to find suitable membership functions for fuzzy association rules based on ant colony systems. In that approach, precision was limited by the use of binary bits to encode the membership functions. This paper elaborates on the original approach to increase the accuracy of results by adding multi-level processing. A multi-level ant colony framework is thus designed and an algorithm based on the structure is proposed to achieve the purpose. The proposed approach first transforms the fuzzy mining problem into a multi-stage graph, with each route representing a possible set of membership functions. The new approach then extends the previous one, using multi-level processing to solve the problem in which the maximum quantities of item values in the transactions may be large. The membership functions derived in a given level will be refined in the subsequent level. The final membership functions in the last level are then outputted to the rule-mining phase to find fuzzy association rules. Experiments are also performed to show the performance of the proposed approach. The experimental results show that the proposed multi-level ant colony systems mining approach can obtain improved results.     

Genetic-fuzzy mining with multiple minimum supports based on fuzzy clustering

Data mining is the process of extracting desirable knowledge or interesting patterns from existing databases for specific purposes. Most of the previous approaches set a single minimum support threshold for all the items and identify the relationships among transactions using binary values. In real applications, different items may have different criteria to judge their importance. In the past, we proposed an algorithm for extracting appropriate multiple minimum support values, membership functions and fuzzy association rules from quantitative transactions. It used requirement satisfaction and suitability of membership functions to evaluate fitness values of chromosomes. The calculation for requirement satisfaction might take a lot of time, especially when the database to be scanned could not be totally fed into main memory. In this paper, an enhanced approach, called the fuzzy cluster-based genetic-fuzzy mining approach for items with multiple minimum supports (FCGFMMS), is thus proposed to speed up the evaluation process and keep nearly the same quality of solutions as the previous one. It divides the chromosomes in a population into several clusters by the fuzzy k-means clustering approach and evaluates each individual according to both their cluster and their own information. Experimental results also show the effectiveness and the efficiency of the proposed approach.     

A genetic-fuzzy mining approach for items with multiple minimum supports

Data mining is the process of extracting desirable knowledge or interesting patterns from existing databases for specific purposes. Mining association rules from transaction data is most commonly seen among the mining techniques. Most of the previous mining approaches set a single minimum support threshold for all the items and identify the relationships among transactions using binary values. In the past, we proposed a genetic-fuzzy data-mining algorithm for extracting both association rules and membership functions from quantitative transactions under a single minimum support. In real applications, different items may have different criteria to judge their importance. In this paper, we thus propose an algorithm which combines clustering, fuzzy and genetic concepts for extracting reasonable multiple minimum support values, membership functions and fuzzy association rules from quantitative transactions. It first uses the k-means clustering approach to gather similar items into groups. All items in the same cluster are considered to have similar characteristics and are assigned similar values for initializing a better population. Each chromosome is then evaluated by the criteria of requirement satisfaction and suitability of membership functions to estimate its fitness value. Experimental results also show the effectiveness and the efficiency of the proposed approach.     

用于模糊控制器设计的遗传算法研究

将遗传操作用于模糊规则和控制器参数编码，实现输入变量的合理组合、模糊规则的获取和控制器参数的优化，设计者仅需给出一个运行遗传算法(GA)的个体适应度函数。同时将模拟退火算法(SA)用于优化控制器参数，这种GASA混合优化策略在模糊控制器设计中取得了良好的效果。实例表明了算法的有效性。     

GA-based learning for rule identification in fuzzy neural networks

Employing an effective learning process is a critical topic in designing a fuzzy neural network, especially when expert knowledge is not available. This paper presents a genetic algorithm (GA) based learning approach for a specific type of fuzzy neural network. The proposed learning approach consists of three stages. In the first stage the membership functions of both input and output variables are initialized by determining their centers and widths using a self-organizing algorithm. The second stage employs the proposed GA based learning algorithm to identify the fuzzy rules while the final stage tunes the derived structure and parameters using a back-propagation learning algorithm. The capabilities of the proposed GA-based learning approach are evaluated using a well-examined benchmark example and its effectiveness is analyzed by means of a comparative study with other approaches. The usefulness of the proposed GA-based learning approach is also illustrated in a practical case study where it is used to predict the performance of road traffic control actions. Results from the benchmarking exercise and case study effectively demonstrate the ability of the proposed three stages learning approach to identify relevant fuzzy rules from a training data set with a higher prediction accuracy than alternative approaches.