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。     

基于GA的非线性系统Fuzzy控制规则自调整

控制精度和自适应能力一直是模糊控制中较难解决的问题,对于非线性系统更是如此,解决这一技术的核心问题在于控制规则的选取,而遗传算法可以较好地解决常规的数学优化技术不能有效解决的问题。该文给出了对于具有修正因子的控制规则,采用遗传算法对其参数进行自调整的方法,以提高整个控制器的性能。仿真结果表明,这种方法可提高模糊控制器的性能,对非线性系统的控制是有效的。     

Hybrid genetic algorithm with adaptive local search scheme

This paper proposes a hybrid genetic algorithm (a-hGA) with adaptive local search scheme. For designing the a-hGA, a local search technique is incorporated in the loop of genetic algorithm (GA), and whether or not the local search technique is used in the GA is automatically determined by the adaptive local search scheme. Two modes of adaptive local search schemes are developed in this paper. First mode is to use the conditional local search method that can measure the average fitness values obtained from the continuous two generations of the a-hGA, while second one is to apply the similarity coefficient method that can measure a similarity among the individuals of the population of the a-hGA. These two adaptive local search schemes are included in the a-hGA loop, respectively. Therefore, the a-hGA can be divided into two types: a-hGA1 and a-hGA2. To prove the efficiency of the a-hGA1 and a-hGA2, a canonical GA (cGA) and a hybrid GA (hGA) with local search technique and without any adaptive local search scheme are also presented. In numerical example, all the algorithms (cGA, hGA, a-hGA1 and a-hGA2) are tested and analyzed. Finally, the efficiency of the proposed a-hGA1 and a-hGA2 is proved by various measures of performance.     

基于遗传算法的有趣模糊规则挖掘算法

针对数据挖掘中多强调分类规则的准确性和可理解性而很少研究规则的有趣性问题。对CHENS和LIUB提出的兴趣规则挖掘方法进行扩展,提出一种基于遗传算法的有趣模糊规则挖掘方法．实验表明该方法是可行的．     

一种改进的区间二型模糊控制器设计

针对二型模糊控制器设计中出现的降型计算方法损失不确定性信息的问题,提出一种改进的区间二型模糊控制器．该控制器在充分利用二型模糊推理结果的前提下,对区间模糊输出进行再次优化,其优化指标可直接与被控系统性能相关,由此可得到更有利于提高系统整体性能的准确输出量．最后,将改进的控制器用于汽车非线性悬架系统的控制,仿真结果验证了所提出方法的有效性．     

A hybrid genetic algorithm with the Baldwin effect

Here we present a new hybrid genetic algorithm (HGA) with the Baldwin effect. In the HGA, a local search is employed to change the fitness of individuals but the acquired improvements do not change the individual itself. This local search step exploits the Baldwin effect. Some numerical applications show that this algorithm can yield the global optimum more efficiently than commonly used HGAs. A theorem is presented that guarantees the convergence in probability of the new HGA.     

基于混合遗传算法的QoS多播路由算法

具有多QoS约束的多播路由问题具有NP完全的复杂度。基于延时、延时抖动、带宽、丢包率等QoS约束,描述了一种适应于研究QoS多播路由的网络模型,提出了基于遗传算法和禁忌搜索混合策略的具有多QoS约束的多播路由算法。该算法充分利用了遗传算法和禁忌搜索的优点。克服了遗传算法在求解多QoS约束多播路由问题中的爬山能力差以及不成熟收敛等问题。仿真实验结果表明,该算法为多QoS约束多播路由问题的求解提供了一种有效的新途径。     

Hybrid genetic algorithm approach for precedence-constrained sequencing problem

The objective of precedence-constrained sequencing problem (PCSP) is to locate the optimal sequence with the shortest traveling time among all feasible sequences. Various methods for effectively solving the PCSP have been suggested. This paper proposes a new concept of hybrid genetic algorithm (HGA) with adaptive local search scheme in order that the PCSP should be effectively solved. By the use of the adaptive local search scheme, the local search is automatically adapted into the loop of genetic algorithm. Two types of the PCSP are presented and analyzed to compare the efficiency among the proposed HGA approach and other competing conventional approaches. Finally, it is proved that the proposed HGA approach outperforms the other competing conventional approaches.     

基于混合遗传算法的订单生产调度在机车车辆行业中的应用

机车车辆行业作为典型的面向订单的机械制造企业,优化的生产调度方法能提高订单的准时交货,缩短产品的生产周期,提高企业的市场竞争力。订单生产调度问题是典型的NP-hard问题。遗传算法(Genetic Algorithms)为求具有多个约束的复杂问题提供了有效的方法。但是遗传算法的局部搜索能力比较差,在解决订单生产调度问题中存在着明显的不足。本文引入了局部搜索能力很强的禁忌搜索算法,用遗传算法和禁忌搜索算法相结合的混合遗传算法来解决机车车辆行业中面向订单生产调度问题。     

基于遗传算法优化的变论域模糊控制在厌氧消化过程的应用

面对具有较强非线性、不确定性和难以建立精确数学模型的控制对象,传统PID控制很难达到理想的控制效果,而模糊控制却是一种非常有效的智能控制方法,但是常规模糊控制精度低,超调量大,同时模糊控制规则的设计存在较强的主观性,难以把握。本文采用遗传算法优化改进的变论域模糊控制中的伸缩因子,当误差较大时,将控制作用的伸缩因子乘以一个较大系数,增加控制作用,提高系统输出,迅速减小误差;当误差较小时(本文取误差小于±0.025),将伸缩因子在原有的基础上乘一个较小的系数,减少控制作用范围,使系统输出趋于平稳,从而使控制规则分布更加合理,减少对专家知识的依赖。实验结果表明,同传统PID控制策略、常规模糊控制策略和常规变论域模糊控制策略相比,无论在动态性能方面,还是在稳态性能方面,改进的变论域模糊控制策略的控制效果均优于其他3种控制策略。     

基于改进遗传算法的TS模糊模型的优化设计

提出了一种新的将隶属度函数和规则库统一编码的改进遗传算法进行TS模糊模型整体优化设计的方法。利用FCM算法和最小二乘法辨识初始的模糊模型;利用改进遗传算法整体优化模糊模型,克服了以往将模型结构和参数分开优化的缺陷。为了提高模型的解释性,提出了将基于相似性的模糊集合和模糊规则的简化方法用于对模型的约简,并利用该方法对Mackey-Glass混沌序列建模。仿真结果验证了该方法的有效性。     

A robust integrator algorithm with genetic based fuzzy controller feedback for direct vector control

The voltage model used for direct vector control has in the flux calculation process an open integration problem, which is generally solved with a feedback loop. In this paper, a new design method is developed for the feedback loop of the integrator. The method, as apart from standards in the literature, uses a fuzzy controller. Fuzzy controllers are knowledge-based systems that include fuzzy rules and fuzzy membership functions to incorporate human knowledge into their knowledge base. The determination of these rules and membership functions is one the key problems when designing fuzzy controllers, and is generally affected by subjective decisions. In this study, a fuzzy controller with rules and membership functions determined by genetic algorithms (GAs) in this study is designed and tested on various motors of different power ratings. The proposed method is simulated by using MATLAB/SIMULINK and implemented on an experimental system using a TMS320C31 digital signal processor.     

基于模糊控制及遗传算法的反向选择算法

针对基于人工免疫的入侵检测技术中所使用的传统反向选择算法,在面对大量的网络通信数据或具有多个分离特征区间网络通信数据时的无效性,提出了基于模糊控制及遗传算法的反向选择算法.在利用反向选择算法生成抗体时,首先利用模糊控制原理来确定抗体的数量,使得计算机中抗体的数量处于最优,然后为了达到在一定数量抗体时种群的总体免疫力最大,引入了遗传算法来进化种群,最终使得在计算机中抗体的数量得到控制,同时在该数量下种群具有最大的免疫力.     

Hybrid learning for interval type-2 fuzzy logic systems based on orthogonal least-squares and back-propagation methods

This paper presents a novel learning methodology based on a hybrid algorithm for interval type-2 fuzzy logic systems. Since only the back-propagation method has been proposed in the literature for the tuning of both the antecedent and the consequent parameters of type-2 fuzzy logic systems, a hybrid learning algorithm has been developed. The hybrid method uses a recursive orthogonal least-squares method for tuning the consequent parameters and the back-propagation method for tuning the antecedent parameters. Systems were tested for three types of inputs: (a) interval singleton, (b) interval type-1 non-singleton, and (c) interval type-2 non-singleton. Experiments were carried out on the application of hybrid interval type-2 fuzzy logic systems for prediction of the scale breaker entry temperature in a real hot strip mill for three different types of coil. The results proved the feasibility of the systems developed here for scale breaker entry temperature prediction. Comparison with type-1 fuzzy logic systems shows that hybrid learning interval type-2 fuzzy logic systems provide improved performance under the conditions tested.     

一种基于模糊逻辑的自适应信标交换算法

针对移动无线传感器网络中贪婪地理路由协议采用固定信标周期导致通信暂盲的问题,提出了一种基于模糊逻辑的自适应信标交换算法。该算法以节点移动速度、节点剩余能量和邻居节点的数量作为评价因素,利用模糊逻辑控制机制确定自适应的信标周期,提高了邻居表构建与维护的准确性与实时性,为贪婪地理转发提供了可靠依据。仿真结果表明：该算法有效减少了通信暂盲现象,降低了控制开销和平均端到端时延,提高了分组交付率,适用于对传输可靠性要求高的大规模移动无线传感器网络。     

Unconstrained handwritten character recognition based on fuzzy logic

This paper presents an innovative approach called box method for feature extraction for the recognition of handwritten characters. In this method, the binary image of the character is partitioned into a fixed number of subimages called boxes. The features consist of vector distance (γ) from each box to a fixed point. To find γ the vector distances of all the pixels, lying in a particular box, from the fixed point are calculated and added up and normalized by the number of pixels within that box. Here, both neural networks and fuzzy logic techniques are used for recognition and recognition rates are found to be around 97 percent using neural networks and 98 percent using fuzzy logic. The methods are independent of font, size and with minor changes in preprocessing, it can be adopted for any language.     

Solving exclusionary side constrained transportation problem by using a hybrid spanning tree-based genetic algorithm

In real life applications we often have the following problem: How to find the reasonable assignment strategy to satisfy the source and destination requirement without shipping goods from any pairs of prohibited sources simultaneously to the same destination so that the total cost can be minimized. This kind of problem is known as the transportation problem with exclusionary side constraint (escTP). Since this problem is one of nonlinear programming models, it is impossible to solve this problem using a traditional linear programming software package (i.e., LINDO). In this paper, an evolutionary algorithm based on a genetic algorithm approach is proposed to solve it. We adopt a Prüfer number to represent the candidate solution to the problem and design the feasibility of the chromosome. Moreover, to handle the infeasible chromosome, here we also propose the repairing procedure. In order to improve the performance of the genetic algorithm, the fuzzy logic controller (FLC) is used to dynamically control the genetic operators. Comparisons with other conventional methods and the spanning tree-based genetic algorithm (st-GA) are presented and the results show the proposed approach to be better as a whole.     

模糊控制器优化方法及其在传感器补偿中的应用

为了减小压力传感器温度漂移造成的测量误差,使用0阶T—S模糊控制器对压力传感器温度附加误差进行校正,校正后的误差为±0.3%,且零位温度系数和灵敏度温度系数都降低1个数量级以上。使用模拟退火算法对模糊规则进行寻优,提高了校正精度,并保持了0阶T—S模糊控制器运算简单和速度快的特点,使之能快速地完成传感器的温度补偿。     

On-line genetic algorithm-based fuzzy-neural sliding mode controller using improved adaptive bound reduced-form genetic algorithm

In this article, a novel on-line genetic algorithm-based fuzzy-neural sliding mode controller trained by an improved adaptive bound reduced-form genetic algorithm is developed to guarantee robust stability and good tracking performance for a robot manipulator with uncertainties and external disturbances. A general sliding manifold, which can be non-linear or time varying, is used to construct a sliding surface and reduce control law chattering. In this article, the sliding surface is used to derive a genetic algorithm-based fuzzy-neural sliding mode controller. To identify structured system dynamics, a B-spline membership function fuzzy-neural network, which is trained by the improved genetic algorithm, is used to approximate the regressor of the robot manipulator. The sliding mode control with a general sliding surface plays the role of a compensator when the fuzzy-neural network does not approximate the dynamics regressor of the robot manipulator well in the transient period. The adjustable parameters of the fuzzy-neural network are tuned by the improved genetic algorithm, which, with the use of the sequential-search-based crossover point method and the single gene crossover, converges quickly to near-optimal parameter values. Simulation results show that the proposed genetic algorithm-based fuzzy-neural sliding mode controller is effective and yields superior tracking performance for robot manipulators.     

基于遗传算法和禁忌搜索的混合优化策略

将禁忌搜索和遗传算法相结合,给出了一种求解优化问题的混合策略--禁忌遗传优化算法.该算法一方面为禁忌搜索找到了较好的初始点,减少了调用禁忌搜索的次数,另一方面也可以克服遗传算法爬山能力差的缺点,从而加快了收敛速度,提高了解的质量.通过实例验证了该优化算法的有效性和可靠性,并将其用于网络拥塞控制的研究中,为进一步实施网络拥塞控制提供了一种有效的途径.