Hierarchical aggregation of multiple fuzzy controllers for global complex control problems

In this paper, we illustrate a proposed method for control that combines the outputs of several individual controllers to improve global control of complex nonlinear plants. In the first part of this paper, we illustrate the proposed method that consists of two levels, where in the top level a fuzzy system represents a superior control that is designed for adjusting the behavior of the individual fuzzy controllers at the lower level. To test the approach, we consider the problem of flight control because it requires several individual controllers. Also a comparison is performed, where the hierarchical control strategy is compared with a simple control approach using the t student test. In this paper, we show that the proposed method outperforms the conventional fuzzy control approach. In the optimal design of the proposed control architecture a genetic algorithm was also applied to tune the parameters of the fuzzy systems in an optimal fashion.     

ON THE MEASURE OF FUZZINESS AND NEGATION Part I: Membership in the Unit Interval

We investigate the meaning of the concept of fuzziness. It is suggested that fuzziness can be related to the lack of distinction between a set and its negation. Using fuzzy subsets with grades of membership in the unit interval, measures of fuzziness are suggested. First, a class of measures based on metric distances is used to derive numeric measures of fuzziness of a fuzzy set. Finally, the concept of compatibility is used to develop linguistic measures of fuzziness.     

Improvement of Grain Size and Dome Height of Microalloyed Steels Using Taguchi Method Based on Grey Relational Grade in Controlled Rolling

An efficient approach was introduced for improving the condition of major controlled rolling process parameters of roughing, finishing and coiling temperatures and optimizing these parameters to obtain minimum grain size and maximum dome height simultaneously. Taguchi method combined with grey relational analysis was applied to achieve optimum grain size and dome height during controlled rolling process. For this purpose, four levels for the above temperatures were chosen and sixteen experiments were conducted based on orthogonal array of Taguchi method. Based on Taguchi approach, signal-to-noise (S/N) ratios were calculated and used in order to obtain the optimum levels for every input parameter. Analysis of variance revealed that finishing and coiling temperatures have the maximum effect on the grain size and dome height of microalloyed steels. The confirmation tests with the optimal levels of parameters indicated that the grain size and dome height of controlled rolled microalloyed steels can be improved effectively through this approach.     

A novel high-order weighted fuzzy time series model and its application in nonlinear time series prediction

Conventional time series forecast models can hardly develop the inherent rules of complex non-linear dynamic systems because the strict assumptions they need cannot always be met in reality, whereas fuzzy time series (FTS) techniques can be used even the records of times series have uncertainty and instability since they do not need strict assumptions. In previous study of FTS, the process of aggregating the past observations and assigning proper weights of fuzzy logical relationship groups are ignored, which may lead to poor forecasting accuracy since they are important aspects in time series prediction and analysis where determination of future trends depends only on past observations. In this paper, a novel high-order FTS model is constructed to make time series forecasting. Specifically, by applying the harmony search intelligence algorithm, the optimal lengths of intervals are tuned. Moreover, regularly increasing monotonic quantifiers are employed on fuzzy sets to obtain the weights of ordered weighted aggregation. Simultaneously, the weights of right-hand side of fuzzy logical relationship groups are explored to compensate the presence of bias in the prediction. In the part of empirical analysis, the developed model was applied to predict three well-known time series: numbers of enrollment of Alabama University, TAIEX and electricity load demand of New South Wales and the results obtained were compared with several counterparts, including some old and recently developed models. Experimental results demonstrate that the developed model cannot only achieve higher accuracy of prediction, but also capture the fuzzy features and characters.     

对《屋面工程技术规范》（GB50345-2012）中若干问题的理解

新修订的《屋面工程技术规范）（GB50345--2012）已由住建部于2012年5月批准发布，并于2012年10月1日起实施。新规范在原规范的基础上进行了较大的修改，包括屋面工程设计、施工按构造层划分章节，修改了屋面防水等级，取消了防水层合理使用年限和刚性防水屋面，增加了消防、节能的有关条文，补充了金属板屋面、玻璃采光顶等新内容。本文对新规范中若干重大修改理由作了较详细的阐述。     

Minors of Bezout matrices,subresultants and the parameterization of the degree of the polynomial greatest common divisor

The main purpose of this article is to present algorithms to parameterize the degree of the greatest common divisor of two polynomials with parametric coefficients: these algorithms are based on the fact that the principal minors of the Bezout matrices provide the principal subresultant sequence. When coefficients depend on parameters, these algorithms show a better behaviour than the classical ones.     

Evaluating the criteria for human resource for science and technology (HRST) based on an integrated fuzzy AHP and fuzzy DEMATEL approach

This study intends to use a combination of fuzzy Analytic Hierarchy Process (AHP) and fuzzy Decision-making Trial and Evaluation Laboratory (DEMATEL) method in human resource for science and technology (HRST). Specifically, this study first uses AHP to evaluate the weighting for each criterion and then use DEMATEL method to establish contextual relationships among those criteria. We find out Infrastructure might be more critical since it is a cause and will directly influence human resource for science and technology performance. For human resource for science and technology (HRST), improving Infrastructure might be a better choice for the long period of time. Moreover, Education, R&D Expenses and Immediate output are more important second-tier criteria than Value, Cooperation, Labor Market, Human Capital and Intermediate output. Therefore, the improvement should be started with Infrastructure, particularly on identification of the Education, R&D Expenses and Immediate output.     

A fast numerical algorithm for solving nearly penta-diagonal linear systems

In this paper, we present a fast numerical algorithm for solving nearly penta-diagonal linear systems and show that the computational cost is less than those of three algorithms in El-Mikkawy and Rahmo, [Symbolic algorithm for inverting cyclic penta-diagonal matrices recursively–Derivation and implementation, Comput. Math. Appl. 59 (2010), pp. 1386–1396], Lv and Le [A note on solving nearly penta-diagonal linear systems, Appl. Math. Comput. 204 (2008), pp. 707–712] and Neossi Nguetchue and Abelman [A computational algorithm for solving nearly penta-diagonal linear systems, Appl. Math. Comput. 203 (2008), pp. 629–634.]. In addition, an efficient way of evaluating the determinant of a nearly penta-diagonal matrix is also discussed. The algorithm is suited for implementation using computer algebra systems (CAS) such as MATLAB, MACSYMA and MAPLE. Some numerical examples are given in order to illustrate the efficiency of our algorithm.     

Soft computing methods applied to combination of one-class classifiers

The paper shows the possibilities of generalizing the two-class classification into multi-class classification by means of a fuzzy inference system. Fuzzy combiner harnesses the support values from classifiers to provide final response having no other restrictions on their structure. We compare proposed combination methods with ECOC and two variations of decision templates, based on Euclidean and symmetric distance. The effectiveness of the proposed combination method based on the fuzzy logic theory is also evaluated via computer experiments carried out on benchmark datasets.     

Performance analysis of fractional order fuzzy PID controllers applied to a robotic manipulator

A two-link robotic manipulator is a Multi-Input Multi-Output (MIMO), highly nonlinear and coupled system. Therefore, designing an efficient controller for this system is a challenging task for the control engineers. In this paper, the Fractional Order Fuzzy Proportional-Integral-Derivative (FOFPID) controller for a two-link planar rigid robotic manipulator for trajectory tracking problem is investigated. Robustness testing of FOFPID controller for model uncertainties, disturbance rejection and noise suppression is also investigated. To study the effectiveness of FOFPID controller, its performance is compared with other three controllers namely Fuzzy PID (FPID), Fractional Order PID (FOPID) and conventional PID. For tuning of parameters of all the controllers, Cuckoo Search Algorithm (CSA) optimization technique was used. Two performance indices namely Integral of Absolute Error (IAE) and Integral of Absolute Change in Controller Output (IACCO) having equal weightage for both the links are considered for minimization. Numerical simulation results clearly indicate the superiority of FOFPID controller over the other controllers for trajectory tracking, model uncertainties, disturbance rejection and noise suppression.