带并行机调度问题中基于ANFIS的自适应算法

针对用规则的线性组合方法解决实际的复杂调度中,如何确定哪些规则需要组合和确定权系数一直缺乏有效的方法,提出了根据多个不同的生产调度目标采用ANFIS将不同调度目标的规则进行自适应的综合,进行学习后产生模糊规则的算法,并将其用于带并行机Jobshop调度问题中。数值计算结果表明,该算法能够较好地对一些规则进行综合,并且在此类调度问题中有比这些规则更好的效果。今后的研究方向是调整ANFIS的结构使其更适合调度问题。     

基于双种群模糊引力搜索算法的舰载机甲板作业调度

舰载机甲板作业调度问题是一类具有NP-hard特性的资源受限多项目调度问题.首先,分析舰载机甲板作业调度问题的工序流程约束和各类资源约束,构建舰载机甲板作业调度混合整数规划模型.然后,基于基本引力搜索算法,提出双种群模糊引力搜索算法用于模型求解.算法采用基于作业时序修正的优先数编码,并采用双种群交替迭代结构,将基于个体的双向对齐技术扩展到种群层面,基于串行调度生成机制产生调度方案.为了提高算法性能,采用边界修正策略修正越界粒子编码,在引力计算阶段,采用模糊逻辑控制策略进行参数自适应控制.最后,通过案例仿真和算法对比验证了双种群模糊引力搜索算法的有效性,所提出的算法适合求解大规模的舰载机甲板作业调度问题.     

基于模糊逻辑系统的输出跟踪控制问题

针对一类未知的非线性互联大系统，设计间接自适应模糊控制器以实现跟踪控制，采用模糊控制，模糊逻辑逼近和模糊滑模控制相结合的方法，对维数较低的子系统未知动态和维数较高的互联项未知动态分别采用两类模糊规则进行逼近，对系统的外部干扰及模糊逼近误差采用模糊滑模控制予以抵消，基于Lyapunov方法实现模糊系统中的参数自适应律并在线调节，所设计的间接自适应控制器使系统在Lyapunov意义下稳定，且跟踪误差趋近于0，仿真结果表明了该设计方法的正确性。     

Logical radial basis function networks a hybrid intelligent model for function approximation

The aim of this article is to introduce a new approach for fuzzy neural network models which can be used effectively in function approximation problems. The proposed model is introduced as an adaptive two-level fuzzy inference system. The architecture of the model is basically a two-layer network of new types of fuzzy-neurons which act as fuzzy IF–THEN rules. The model can be considered as a logical version of the Radial Basis Function networks (RBF). Genetic Algorithms have been adopted as the learning mechanism of the proposed model. Simulations show both highly nonlinear mapping and reasoning capabilities together with simpler structure and better performance when compared with classical neural networks.     

Learning-based scheduling of flexible manufacturing systems using case-based reasoning

A common method of dynamically scheduling jobs in Flexible Manufacturing Systems (FMSs) is to employ dispatching rules. However, the problem associated with this method is that the performance of the rules depends on the state of the system, but there is no rule that is superior to all the others for all the possible states the system might be in. It would therefore be highly desirable to employ the most suitable rule for each particular situation. To achieve this, this paper presents a scheduling approach that uses Case-Based Reasoning (CBR), which analyzes the system's previous performance and acquires "scheduling knowledge," which determines the most suitable dispatching rule at each particular moment in time. Simulation results indicate that the proposed approach produces significant performance improvements over existing dispatching rules.     

Fuzzy Based Ant Colony Optimization Scheduling in Cloud Computing

Cloud computing is an Information Technology deployment model established on virtualization. Task scheduling states the set of rules for task allocations to an exact virtual machine in the cloud computing environment. However, task scheduling challenges such as optimal task scheduling performance solutions, are addressed in cloud computing. First, the cloud computing performance due to task scheduling is improved by proposing a Dynamic Weighted Round-Robin algorithm. This recommended DWRR algorithm improves the task scheduling performance by considering resource competencies, task priorities, and length. Second, a heuristic algorithm called Hybrid Particle Swarm Parallel Ant Colony Optimization is proposed to solve the task execution delay problem in DWRR based task scheduling. In the end, a fuzzy logic system is designed for HPSPACO that expands task scheduling in the cloud environment. A fuzzy method is proposed for the inertia weight update of the PSO and pheromone trails update of the PACO. Thus, the proposed Fuzzy Hybrid Particle Swarm Parallel Ant Colony Optimization on cloud computing achieves improved task scheduling by minimizing the execution and waiting time, system throughput, and maximizing resource utilization.     

Design of an adaptive fuzzy sliding mode control for uncertain discrete-time nonlinear systems based on noisy measurements

This paper presents the design of an adaptive fuzzy sliding mode control (AFSMC) for uncertain discrete-time nonlinear dynamic systems. The dynamic systems are described by a discrete-time state equation with nonlinear uncertainties, and the uncertainties include the modelling errors and the external disturbances to be unknown but nonlinear with the bounded properties. The states are measured by the restriction of measurement sensors and the contamination with independent measurement noises. The nonlinear uncertainties are approximated by using the fuzzy IF-THEN rules based on the universal approximation theorem, and the approximation error is compensated by adding an adaptive complementary term to the proposed AFSMC. The fuzzy inference approach based on the extended single input rule modules is proposed to reduce the number of the fuzzy IF-THEN rules. The estimates for the un-measurable states and the adjustable parameters are obtained by using the weighted least squares estimator and its simplified one. It is proved that under some conditions the estimation errors will remain in the vicinity of zero as time increases, and the states are ultimately bounded subject to the proposed AFSMC. The effectiveness of the proposed method is indicated through the simulation experiment of a simple numerical system.     

Fuzzy resource‐constrained project scheduling using taboo search algorithm

This paper proposes a mathematical model to deal with project scheduling problem under vagueness and a framework of a heuristic approach to fuzzy resource‐constrained project scheduling problem (F‐RCPSP) using heuristic and metaheuristic scheduling methods. Our approach is very simple to apply, and it does not require knowing the explicit form of the membership functions of the fuzzy activity times. We first identify two typical activity priority rules, namely, resource over time and minimum slack priority rules. They are used in the F‐RCPS problem and in the initial solution of Taboo search (TS) method. We improved the TS algorithm method for the solution of F‐RCPSP. Our objective is to check the performance of these rules and metaheuristic method in minimizing the project completion time for the F‐RCPS problems. In our study, we use trapezoidal fuzzy numbers (TraFNs) for activity times and activity‐on‐nodes (AON) representation and compute several project characteristics such as earliest, latest, and slack times in terms of TraFNs. The computational experiment shows that the performance of the proposed TS is better than the evaluation and light beam search algorithms in the literature. © 2012 Wiley Periodicals, Inc.     

Learning fuzzy inference systems using an adaptive membershipfunction scheme

An adaptive membership function scheme for general additive fuzzy systems is proposed in this paper. The proposed scheme can adapt a proper membership function for any nonlinear input-output mapping, based upon a minimum number of rules and an initial approximate membership function. This parameter adjustment procedure is performed by computing the error between the actual and the desired decision surface. Using the proposed adaptive scheme for fuzzy system, the number of rules can be minimized. Nonlinear function approximation and truck backer-upper control system are employed to demonstrate the viability of the proposed method.     

Design of fuzzy controllers with adaptive rule insertion

In this paper, an approach of designing adaptive fuzzy controllers is presented to systematically develop efficient and effective rules for fuzzy controllers. The proposed fuzzy controllers are first designed with two basic fuzzy if-then rules. Then according to the design requirements of the fuzzy control system, new fuzzy if-then rules are inserted into the rule-base structure of the fuzzy controller. Initially the inserted fuzzy rules are redundant in the sense that the resultant input-output mapping of the fuzzy rules remains intact. After that the parameters of the membership functions for the fuzzy sets of the newly added fuzzy rules are trained on-line to minimize predefined cost functions. Thus, efficient fuzzy controllers can be systematically designed. Simulations for linear, nonlinear, and delayed systems are provided to show the effectiveness of the proposed approach.     

Fuzzy-Based Dynamic Distributed Queue Scheduling for Packet Switched Networks

Addressing the problem of queue scheduling for the packet-switched system is a vital aspect of congestion control. In this paper, the fuzzy logic based decision method is adopted for queue scheduling in order to enforce some level of control for traffic of different quality of service requirements using predetermined values. The fuzzy scheduler proposed in this paper takes into account the dynamic nature of the Internet traffic with respect to its time-varying packet arrival process that affects the network states and performance. Three queues are defined, viz low, medium and high priority queues. The choice of prioritizing packets influences how queues are served. The fuzzy scheduler not only utilizes queue priority in the queue scheduling scheme, but also considers packet drop susceptibility and queue limit. Through simulation it is shown that the fuzzy scheduler is more appropriate for the dynamic nature of Internet traffic in a packet-switched system as compared with some existing queue scheduling methods. Results show that the scheduling strategy of the proposed fuzzy scheduler reduces packet drop, provides good link utilization and minimizes queue delay as compared with the priority queuing (PQ), first-in-first-out (FIFO), and weighted fair queuing (WFQ).     

溶解氧浓度的直接自适应动态神经网络控制方法

针对污水处理过程溶解氧浓度的控制问题,提出一种直接自适应动态神经网络控制方法(direct adaptive dynamic neural network control,DADNNC).构建的控制系统主要包括神经网络控制器和补偿控制器.神经网络控制器由自组织模糊神经网络实现系统状态与控制量之间的映射;提出一种基于规则无用率的结构修剪算法,并给出结构调整后网络收敛的理论证明.同时,为保证系统稳定,设计补偿控制器减小网络逼近误差,参数调整由Layapunov理论给出.国际基准仿真平台上的实验表明,与固定结构神经网络控制器、PID和模型预测控制等已有控制方法相比,DADNNC方法具有更高的控制精度和更强的适应能力.     

变论域自适应模糊控制及其在Chua's混沌电路中的应用

本文研究输出反馈自适应变论域模糊控制方法.变论域模糊控制通过自适应调节伸缩因子,生成大量规则,提高了系统的控制精度.由于状态的不完全可测,本文首先通过构造状态观测器实现输出反馈控制.然后,为了抑制外部扰动和参数变化,通过监督控制将系统的状态约束在给定的范围之内,从而提高了控制器的精度和鲁棒性.进而利用Lyapunov函数证明了观测器-控制器系统的稳定性;在所有状态一致有界的前提下,整个自适应控制算法保证闭环系统的稳定性.最后将所提算法应用于Chua s混沌电路,仿真结果证明了控制方法的有效性.     

Adaptive fuzzy backstepping output feedback control of nonlinear time-delay systems with unknown high-frequency gain sign

In this paper, an adaptive fuzzy robust feedback control approach is proposed for a class of single-input and single-output (SISO) strict-feedback nonlinear systems with unknown nonlinear functions, time delays, unknown high-frequency gain sign, and without the measurements of the states. In the backstepping recursive design, fuzzy logic systems are employed to approximate the unknown smooth nonlinear functions, K-filters is designed to estimate the unmeasured states, and Nussbaum gain functions are introduced to solve the problem of unknown sign of high-frequency gain. By combining adaptive fuzzy control theory and adaptive backstepping design, a stable adaptive fuzzy output feedback control scheme is developed. It has been proven that the proposed adaptive fuzzy robust control approach can guarantee that all the signals of the closed-loop system are uniformly ultimately bounded and the tracking error can converge to a small neighborhood of the origin by appropriately choosing design parameters. Simulation results have shown the effectiveness of the proposed method.     

基于粒子群优化的集气管压力变结构模糊控制

针对某焦炉集气管压力波动幅值大、压力振荡剧烈的情况,提出了一种基于粒子群优化的变结 构模糊控制方法．该方法针对集气管压力的不同波动范围,采用不同的控制规则设计了两个模糊控制器．针 对模糊量化因子调节的困难,采用粒子群优化惯性系数的自适应调整机制,以寻优模糊控制器量化因子．仿 真实验以及实际运行结果表明,采用该方法所建立的控制系统能够快速调节3#阀门开度,使集气管压力在短 时间内达到稳定,证明了该算法的有效性和优越性．     

Real time fuzzy scheduling rules in FMS

This paper presents a real-time fuzzy expert system to scheduling parts for a flexible manufacturing system (FMS). First, some vagueness and uncertainties in scheduling rules are indicated and then a fuzzy-logic approach is proposed to improve the system performance by considering multiple performance measures. This approach focuses on characteristics of the system's status, instead of parts, to assign priorities to the parts waiting to be processed. Secondly, a simulation model is developed and it has shown that the proposed fuzzy logic-based decision making process keeps all performance measures at a good level. The proposed approach provides a promising alternative framework in solving scheduling problems in FMSs, in contrast to traditional rules, by making use of intelligent tools.     

D-FNN based soft-sensor modeling and migration reconfiguration of polymerizing process

A soft-sensor modeling method based on dynamic fuzzy neural network (D-FNN) is proposed for forecasting the key technology indicator convention velocity of vinyl chloride monomer (VCM) in the polyvinylchloride (PVC) polymerizing process. Based on the problem complexity and precision demand, D-FNN model can be constructed combining the system prior knowledge. Firstly, kernel principal component analysis (KPCA) method is adopted to select the auxiliary variables of soft-sensing model in order to reduce the model dimensionality. Then a hybrid structure and parameters learning algorithm of D-FNN is proposed to achieve the favorable approximation performance, which includes the rule extraction principles, the classification learning strategy, the precedent parameters arrangements, the rule trimming technology based on error descendent ratio and the consequent parameters decision based on extended Kalman filter (EKF). The proposed soft-sensor model can automatically determine if the fuzzy rules are generated/eliminated or not so as to realize the nonlinear mapping between input and output variables of the discussed soft-sensor model. Model migration method is adopted to realize the on-line adaptive revision and reconfiguration of soft-sensor model. In the end, simulation results show that the proposed model can significantly enhance the predictive accuracy and robustness of the technical-and-economic indexes and satisfy the real-time control requirements of PVC polymerizing production process.     

一种开放混合实时系统的开放自适应调度算法

开放计算环境下的实时与非实时任务不确定并发,以及多种实时约束混合的复杂约束系统,即开放混合实时系统的需求越来越广泛.通过引入接收控制、调度服务器、自适应调节机制,提出一种开放环境下的自适应实时系统调度架构--OARtS(open adaptive real-time scheduling).它能适应开放计算环境的不确定性,有控制地接受实时任务运行;可根据系统空闲计算带宽变化,自适应地调节任务的实时等级,使得系统运行在最优的实时性能上;对于软实时任务,可根据其计算带宽需求变化,自适应地调节其计算带宽分配,以适应任务执行时间时变引起的实时不确定性.     

区间Type-2 T-S间接自适应模糊控制

本文在Type-1 T-S间接自适应模糊控制器的基础上,利用Type-2模糊系统理论,提出了区间Type-2 T-S间接自适应模糊控制器的设计方法.由于该系统的规则前件是区间Type-2模糊集合,后件为精确数,使构造的控制方法既具备Type-2模糊集处理诸多不确定性的特点,能够减少由于规则不确定对系统的影响,同时又具有T-S模糊模型后件为各输入变量的线性组合的特点,可以提高系统的建模精度,减少系统的规则数等优点.本文利用Lyapunov合成方法,研究了在所有变量一致有界的意义下,闭环系统的全局稳定性,分析了区间Type-2 T-S间接自适应模糊控制系统的收敛性,并给出了系统参数的自适应律.通过倒立摆跟踪模型进行仿真,验证其有效性和优越性.     

Hybrid fuzzy control of robotics systems

This paper presents a new approach towards optimal design of a hybrid fuzzy controller for robotics systems. The salient feature of the proposed approach is that it combines the fuzzy gain scheduling method and a fuzzy proportional-integral-derivative (PID) controller to solve the nonlinear control problem. The resultant fuzzy rule base of the proposed controller can be decomposed into two layers. In the upper layer, the gain scheduling method is incorporated with a Takagi-Sugeno (TS) fuzzy logic controller to linearize the robotics system for a given reference trajectory. In the lower layer, a fuzzy PID controller is derived for all the locally linearized systems by replacing the conventional PI controller by a linear fuzzy logic controller, which has different gains for different linearization conditions. Within the guaranteed stability region, the controller gains can be optimally tuned by genetic algorithms. Simulation studies on a pole balancing robot and a multilink robot manipulator demonstrate the effectiveness and robustness of the proposed approach.