A fuzzy adaptive learning control network with on-line structure and parameter learning

This paper addresses a general connectionist model, called Fuzzy Adaptive Learning Control Network (FALCON), for the realization of a fuzzy logic control system. An on-line supervised structure/parameter learning algorithm is proposed for constructing the FALCON dynamically. It combines the backpropagation learning scheme for parameter learning and the fuzzy ART algorithm for structure learning. The supervised learning algorithm has some important features. First of all, it partitions the input state space and output control space using irregular fuzzy hyperboxes according to the distribution of training data. In many existing fuzzy or neural fuzzy control systems, the input and output spaces are always partitioned into "grids". As the number of input/output variables increase, the number of partitioned grids will grow combinatorially. To avoid the problem of combinatorial growing of partitioned grids in some complex systems, the proposed learning algorithm partitions the input/output spaces in a flexible way based on the distribution of training data. Second, the proposed learning algorithm can create and train the FALCON in a highly autonomous way. In its initial form, there is no membership function, fuzzy partition, and fuzzy logic rule. They are created and begin to grow as the first training pattern arrives. The users thus need not give it any a priori knowledge or even any initial information on these. In some real-time applications, exact training data may be expensive or even impossible to obtain. To solve this problem, a Reinforcement Fuzzy Adaptive Learning Control Network (RFALCON) is further proposed. The proposed RFALCON is constructed by integrating two FALCONs, one FALCON as a critic network, and the other as an action network. By combining temporal difference techniques, stochastic exploration, and a proposed on-line supervised structure/parameter learning algorithm, a reinforcement structure/parameter learning algorithm is proposed, which can construct a RFALCON dynamically through a reward/penalty signal. The ball and beam balancing system is presented to illustrate the performance and applicability of the proposed models and learning algorithms.     

Statistical-Fuzzy Approach to Quantify Cumulative Impact of Change Orders

This paper presents a hybrid approach to quantify the impact of change orders on construction projects using statistical regression and fuzzy logic. There are many qualitative variables affecting the impact of change orders on labor productivity; statistical analysis falls short of addressing the fuzziness of those variables. Because of their complementary nature, fuzzy logic and regression analysis can be integrated; regression analysis is used to determine the membership functions of the input linguistic values. In this paper, each input variable is statistically treated before entering a general rule relating its space to the space of loss in labor productivity. The relative weight of each input variable is determined by its coefficient of determination (R2) value. The expected loss of labor productivity and its standard deviation are then determined from the output fuzzy membership function. The proposed methodology is general and can be applied in areas of system analysis and decision making when a complex input-output function is to be predicted in the presence of some fuzzy knowledge and a large number of real input-output data.     

Bond Behavior of Near-Surface Mounted FRP Strips Bonded to Modern Clay Brick Masonry Prisms: Influence of Strip Orientation and Compression Perpendicular to the Strip

In this paper the results of 18 pull tests performed on clay brick masonry prisms strengthened with near-surface mounted carbon fiber-reinforced polymer (CFRP) strips are presented. The pull tests were designed to add to the existing database and investigate variables significant to masonry construction. FRP was bonded to solid clay brick masonry; FRP aligned both perpendicular and parallel to the bed joint; and in the case of FRP reinforcement aligned parallel to the bed joint, compression applied perpendicular to the strip was used to simulate vertical compression load in masonry walls. Results including bond strength, critical bond length, and the local bond-slip relationship are presented as well as a discussion on the effect of the new variables on these results.     

无数学模型的非线性约束单目标系统优化方法改进

为提高无法准确建立数学模型的非线性约束单目标系统优化问题的寻优精度,并考虑获取样本的代价,提出一种基于支持向量机和免疫粒子群算法的组合方法(support vector machine and immune particle swarm optimization,SVM-IPSO).首先,运用支持向量机构建非线性约束单目标系统预测模型,然后,采用引入了免疫系统自我调节机制的免疫粒子群算法在预测模型的基础上对系统寻优.与基于BP神经网络和粒子群算法的组合方法(BP and particle swarm optimization,BP-PSO)进行仿真实验对比,同时,通过减少训练样本,研究了在训练样本较少情况下两种方法的寻优效果.实验结果表明,在相同样本数量条件下,SVM-IPSO方法具有更高的优化能力,并且当样本数量减少时,相比BP-PSO方法,SVM-IPSO方法仍能获得更稳定且更准确的系统寻优值.因此,SVM-IPSO方法为实际中此类问题提供了一个新的更优的解决途径.      

Self-Adaptive Fitness Formulation for Evolutionary Constrained Optimization of Water Systems

In design of water distribution networks, there are several constraints that need to be satisfied; supplying water at an adequate pressure being the main one. In this paper, a self-adaptive fitness formulation is presented for solving constrained optimization of water distribution networks. The method has been formulated to ensure that slightly infeasible solutions with a low objective function value remain fit. This is seen as a benefit in solving highly constrained problems that have solutions on one or more of the constraint bounds. In contrast, solutions well outside the constraint bounds are seen as containing little genetic information that is of use and are therefore penalized. In this method, the dimensionality of the problem is reduced by representing the constraint violations by a single infeasibility measure. The infeasibility measure is used to form a two-stage penalty that is applied to infeasible solutions. The performance of the method has been examined by its application to two water distribution networks from literature. The results have been compared with previously published results. It is shown that the method is able to find optimum solutions with less computational effort. The proposed method is easy to implement, requires no parameter tuning, and can be used as a fitness evaluator with any evolutionary algorithm. The approach is also robust in its handling of both linear and nonlinear equality and inequality constraint functions. Furthermore, the method does not require an initial feasible solution, this being an advantage in real-world applications having many optimization variables.     

基于模糊神经网络的地图匹配算法

提出了基于模糊神经网络的新的地图匹配算法.该算法综合了数字道路信息和GPS/DR定位信息,提取两个重要参数作为输入变量,即定位点到候选路段的投影距离及定位航向与候选路段方位角差.设计出了四层模糊神经网络及改进的收敛学习规则.实验结果表明所提出的算法能很好地匹配车辆行驶路段位置.      

基于分段模糊Lyapunov函数的轮式移动机器人轨迹跟踪控制

研究了具有控制输入约束和外部干扰的轮式移动机器人的轨迹跟踪问题.在轨迹跟踪位姿误差的T-S模型和并行分布补偿框架下,利用分段模糊Lyapunov理论给出了满足控制输入约束的H_∞控制器设计方法,并证明了闭环系统的稳定性.仿真结果验证了所提方法的有效性.      

Multiobjective Evolutionary Algorithm for Designing Fuzzy-Based Missile Guidance Laws

In this paper, a strength Pareto evolutionary algorithm based approach is proposed for designing a multistage fuzzy-based guidance law which consists of three fuzzy controllers. Each of these controllers is activated in a region of the interception. The distribution of the membership functions and the rules are obtained by solving a nonlinear constrained multiobjective optimization problem where final time, energy consumption, and miss distance are treated as competing objectives. A hierarchical clustering technique is implemented to provide the decision maker with a representative and manageable Pareto-optimal set without destroying the characteristics of the trade-off front. Moreover, a fuzzy-based mechanism is employed to extract the best compromise solution over the trade-off curve. The simulation results show that the proposed design technique was able to generate a missile guidance law which has better performance than the classical proportional guidance law.     

Optimal Channel Cross Section with Composite Roughness

For channels with composite roughness, an equivalent uniform roughness coefficient and flow geometric elements are used in an optimal design method using the Manning equation. The optimal design problems are formulated in a nonlinear optimization framework with the objective function being a cost function per unit length of the canal. Constraints are the Manning equation, positive values for design variables, and specified values of side slopes or top width. The constrained problem is transformed into an unconstrained problem using the Lagrangian multipliers. To obtain an optimal solution for the resulting unconstrained problem, the first-order necessary conditions for optima are applied. The resulting simultaneous nonlinear equations are solved using the computational methodology developed. This technique is applied to illustrative numerical examples. The evaluations establish the potential applicability of the developed computational methodology for optimal design of open channel cross sections with composite roughness.     

Prototype Robotic Masonry System

The introduction of robots and automation technology to construction operations will require considerable efforts to understand the mechanics of the many tasks better as they relate to building structures. To control arms, grippers, nozzles, and other production devices automatically, the mechanics of tools as they interact and work with the different construction materials requires extensive knowledge. One approach to learn about the behavior of all the components of an automated system for construction is to build scaled prototypes that are able to handle the actual material. This paper describes the design of an automated masonry system for prefabricating brick panels. It discusses the conditions that affect the bond strength of the masonry. It then describes the design and development of a Cartesian robot actuator and scaled‐down mortar spreader system built to test the bond strengths and accuracy of robotically placed bricks. The actual fabrication of the partially functional prototype helped the researchers to gain hands‐on familiarity with the many real‐world obstacles in using automation and robotics in construction. However, the learning experience also included an appreciation of the potentials offered by using advanced technologies in construction.     

Cloud Neural Fuzzy PID Hybrid Integrated Algorithm of Flatness Control

In connection with the characteristics of multi-disturbance and nonlinearity of a system for flatness control in cold rolling process, a new intelligent PID control algorithm was proposed based on a cloud model, neural network and fuzzy integration. By indeterminacy artificial intelligence, the problem of fixing the membership functions of input variables and fuzzy rules was solved in an actual fuzzy system and the nonlinear mapping between variables was implemented by neural network. The algorithm has the adaptive learning ability of neural network and the indetermi- nacy of a cloud model in processing knowledge, which makes the fuzzy system have more persuasion in the process of knowledge inference, realizing the online adaptive regulation of PID parameters and avoiding the defects of the traditional PID controller. Simulation results show that the algorithm is simple, fast and robust with good control performance and application value.     

自适应模糊模型在非线性系统中的仿真研究

针对一类仿射单输入单输出非线性系统采用模糊控制、模糊逻辑系统逼近和滑模控制相结合的模糊控制器,利用滑模控制及Lyapunov函数方法提出了直接模糊自适应控制方案。直接自适应控制器充分利用模糊控制规则,通过仿真表明该方法具有较好的跟踪性,能保证闭环系统稳定,跟踪误差收敛,具有一定的鲁棒性,系统中所涉及的所有变量有界,系统的跟踪误差渐近收敛于零或零的一个邻域内。     

Comparison of Neural and Conventional Approaches to Mode Choice Analysis

This paper describes a new approach to behavioral mode choice modeling using neurofuzzy models. The new approach combines the learning ability of artificial neural networks and the transparent nature of fuzzy logic. The approach is found to be highly adaptive and efficient in investigating nonlinear relationships among different variables. In addition, the approach only selects the variables that significantly influence the mode choice and displays the stored knowledge in terms of fuzzy linguistic rules. This allows the modal decision-making process to be examined and understood in great detail. The neurofuzzy model is tested on the U.S. freight transport market using information on individual shipper and individual shipments. Shipments are disaggregated at the five-digit Standard Transportation Commodity Code level. Results obtained from this exercise are compared with similar results obtained from the conventional logit mode choice model and the standard back-propagation artificial neural network. The advantages of using the neurofuzzy approach are described.     

Computational Analysis of Masonry Structures with a Funicular Model

This paper presents a computational approach for the assessment of masonry structures based on the well known analogy between the equilibrium of arches and that of hanging strings or cables working in tension. According to the analogy, the hanging strings model the inverted shape of the equilibrium lines (or thrust lines) describing the locus of the equilibrium forces acting across the sections of the arch. The approach proposed combines two developments. First, a new cable element is proposed to numerically model the strings used to describe the equilibrium lines. The formulation proposed, obtained as a modification of the conventional equations for inextensible cables, is based on an exact analytical derivation. Compared to other available numerical approaches, it has the advantage of ensuring the exact equilibrium of the cable net after deformation. Second, complementary algorithms are proposed for the assessment of the strength of masonry structures by the application of the limit theorems of plasticity (static approach). These algorithms are intended to find optimized solutions complying with the so-called safe (or lower-bound) and uniqueness theorems. Two examples of application are described to illustrate the accuracy of the method and its ability to handle masonry structural systems.     

模糊神经网络融合建模方法及其在轧制力控制中的应用

针对目前热轧中神经网络控制模型不能满足一些特殊轧制规律钢种精度要求的问题,在深入研究现有热轧模型建立与优化的基础上,结合模糊控制技术,提出在神经网络的基础上建立基于模糊规则补偿模型的融合建模方法。针对两类特殊钢种的特性,详细阐述了基于模糊规则补偿模型的建立及实际应用过程,并根据实际生产经验给出建模中规则库的建立过程。实际生产过程应用结果表明,所提出的模糊神经网络融合建模方法可以有效提高轧制力计算精度和厚度控制精度,从而提高热轧带钢产品质量。     

Optimal Design with Probabilistic Objective and Constraints

Significant challenges are associated with solving optimal structural design problems involving the failure probability in the objective and constraint functions. In this paper, we develop gradient-based optimization algorithms for estimating the solution of three classes of such problems in the case of continuous design variables. Our approach is based on a sequence of approximating design problems, which is constructed and then solved by a semiinfinite optimization algorithm. The construction consists of two steps: First, the failure probability terms in the objective function are replaced by auxiliary variables resulting in a simplified objective function. The auxiliary variables are determined automatically by the optimization algorithm. Second, the failure probability constraints are replaced by a parametrized first-order approximation. The parameter values are determined in an adaptive manner based on separate estimations of the failure probability. Any computational reliability method, including first-order reliability and second-order reliability methods and Monte Carlo simulation, can be used for this purpose. After repeatedly solving the approximating problem, an approximate solution of the original design problem is found, which satisfies the failure probability constraints at a precision level corresponding to the selected reliability method. The approach is illustrated by a series of examples involving optimal design and maintenance planning of a reinforced concrete bridge girder.     

Fuzzy Logic Integrated Genetic Programming for Optimization and Design

A fuzzy logic integrated genetic programming (GP) based methodology is proposed to increase the performance of the GP based approach for structural optimization and design. Fuzzy set theory is employed to deal with the imprecise and vague information, especially the design constraints, during the structural design process. A fuzzy logic based decision-making system incorporating expert knowledge and experience is used to control the iteration process of genetic search. Illustrative examples have been used to demonstrate that, when comparing the proposed fuzzy logic controlled GP approach with the pure GP method, the proposed new approach has a higher search efficiency.     

A continuous penalty function method for inverse treatment planning

Conventional inverse treatment planning attempts to calculate dose distributions that may not be feasible given the specified dose levels to various anatomical structures. A technique for inverse treatment planning has been developed that uses only target dose levels which are easily selectable to be feasible. A nonlinear constrained minimization problem is formulated to reflect the goal of sparing critical organs as much as possible while delivering a certain target dose within specified uniformity. The objective function is the squared dose delivered to critical organs. The constraints require the delivery of certain target dose within specified uniformity and non-negative pencil beam weights. A continuous penalty function method is introduced as a method for solving the large-scale constrained minimization problem. The performance of the continuous penalty function method is optimized by numerical investigation of few numerical integration schemes and a pair of weighting functions which influence the utility of the method. Clinical examples are presented that illustrate several features of the technique. The properties of the continuous penalty function method suggest that it may be a viable alternative to conventional inverse treatment planning.     

Lagrangian Approach to Structural Collapse Simulation

Computer analysis of structures has traditionally been carried out using the displacement method combined with an incremental iterative scheme for nonlinear problems. In this paper, a Lagrangian approach is developed, which is a mixed method, where besides displacements, the stress resultants and other variables of state are primary unknowns. The method can potentially be used for the analysis of collapse of structures subjected to severe vibrations resulting from shocks or dynamic loads. The evolution of the structural state in time is provided a weak formulation using Hamilton’s principle. It is shown that a certain class of structures, known as reciprocal structures, has a mixed Lagrangian formulation in terms of displacements and internal forces. The form of the Lagrangian is invariant under finite displacements and can be used in geometric nonlinear analysis. For numerical solution, a discrete variational integrator is derived starting from the weak formulation. This integrator inherits the energy and momentum conservation characteristics for conservative systems and the contractivity of dissipative systems. The integration of each step is a constrained minimization problem and it is solved using an augmented Lagrangian algorithm. In contrast to the traditional displacement-based method, the Lagrangian method provides a generalized formulation which clearly separates the modeling of components from the numerical solution. Phenomenological models of components, essential to simulate collapse, can be incorporated without having to implement model-specific incremental state determination algorithms. The state variables are determined at the global level by the optimization method.     

Spatio-temporal EEG source localization using simulated annealing

The estimation of multiple dipole parameters in spatio-temporal source modeling (STSM) of electroencephalographic (EEG) data is a difficult nonlinear optimization problem due to multiple local minima in the cost function. A straightforward iterative optimization approach to such a problem is very susceptible to being trapped in a local minimum, thereby resulting in incorrect estimates of the dipole parameters. In this paper, we present and evaluate a more robust optimization approach based on the simulated annealing algorithm. The complexity of this approach for the STSM problem was reduced by separating the dipole parameters into linear (moment) and nonlinear (location) components. The effectiveness of the proposed method and its superiority over the traditional nonlinear simplex technique in escaping local minima were tested and demonstrated through computer simulations. The annealing algorithm and its implementation for multidipole estimation are also discussed. We found the simulated annealing approach to be 7-31% more effective than the simplex method at converging to the true global minimum for a number of different kinds of three-dipole problems simulated in this work. In addition, the computational cost of the proposed approach was only marginally higher than its simplex counterpart. The annealing method also yielded similar solutions irrespective of the initial guesses used. The proposed simulated annealing method is an attractive alternative to the simplex method that is currently more common in dipole estimation applications.