lucas

In this paper, we show that an L1-2-type surface in the three-dimensional hyperbolic space H3⊂R41 either is an open piece of a standard Riemannian product H1(−1−−−−−√)×S1(r) , or it has non constant mean curvature, non constant Gaussian curvature, and non constant principal curvatures.

In this paper, we introduce a Takagi-Sugeno (TS) fuzzy model which is derived from a typical Multi-Layer Perceptron Neural Network (MLP NN). At first, it is shown that the considered MLP NN can be interpreted as a variety of TS fuzzy model. It is discussed that the utilized Membership Function (MF) in such TS fuzzy model, despite its flexible structure, has some major restrictions. After modifying the MF, we introduce a TS fuzzy model whose MFs are tunable near and far from focal points, separately. To identify such TS fuzzy model, an incremental learning algorithm, based on an efficient space partitioning technique, is proposed. Through an illustrative example, the methodology of the learning algorithm is explained. Next, through two case studies: approximation of a nonlinear function for a sun sensor and identification of a pH neutralization process, the superiority of the introduced TS fuzzy model in comparison to some other TS fuzzy models and MLP NN is shown.

The advent of cybertechnologies has led to radical paradigm shifts in our social, economic, cultural, and psychological conceptualizations. The paper investigates the advent of cyborgs, indeed our transformation into cyborgs, and the impact of this transformation upon identities and face management, which has an important social value since the late modernism. Cyborg is a designation for the inhabitants of the new environment: the cyberspace. They represent manifold boundary pollutions. The idea of cyberspace already marks the fusion of the real with the imagined and the fantastic. This new virtual space is being inhabited with a new kind of actors: intelligent agents. A fusion of animal and machine. The boundary between human and non- human is also being transgressed. The problematic concept of selfhood and the related technologies constitute the subject of this discourse. It is argued that this process of confusion and corruption can be viewed as also a process of breaking down monological communication and totality.

In this paper a brushless permanent magnet motor is designed considering minimum thrust ripple and maximum thrust density (the ratio of the thrust to permanent magnet volumes). Particle Swarm Optimization (PSO) is used as optimization method. Finite element analysis (FEA) is carried out base on the optimized and conventional geometric dimensions of the motor. The results of the FEA deal to the significant improvement of the all objective functions.

Predicting future behavior of chaotic time series system is a challenging area in the literature of nonlinear systems. The prediction''s accuracy of chaotic time series is extremely dependent on the model and the learning algorithm. On the other hand the cyclic solar activity as one of the natural chaotic systems has significant effects on earth, climate, satellites and space missions. Several methods have been introduced for prediction of solar activity indices especially the sunspot number, which is a common measure of solar activity. In this paper, the problem of embedding dimension estimation for solar activity chaotic time series based on polynomial models is considered. The optimality of embedding dimension has an important role in computational efforts, Lyapunov exponents'' analysis and efficiency of prediction. The method of this paper is based on the fact that the reconstructed dynamics of an attractor should be a smooth map, i.e. with no self intersection in the reconstructed attractor. To check this property, a local general polynomial autoregressive model is fitted to the given data and a canonical state space realization is considered. Then, the normalized one-step forward prediction error for different orders and various degrees of nonlinearity in polynomials is evaluated. Besides the estimation of the embedding dimension, a predictive model is obtained which can be used for prediction and estimation of the Lyapunov exponents. This algorithm is applied to indicate the minimum embedding dimension of sunspot numbers (SSN), Disturbance Storm Time or Dst. and Proton Flux indices are some of the most important among solar activity indices and results depict the power of the proposed method in embedding dimension estimation.

Real Coded Genetic Algorithm, RCGA, is the type of GA which operates on chromosomes with real valued parameters. Different mutation and crossover operations are defined for RCGA. One usable crossover for this kind of GA is to consider its chromosomes simply as bit strings and utilize the same operations as Binary Coded GA. In this paper, we attempt to show that this kind of crossover can not hasten the convergence process unless the break points fall at the boundaries of parameters in the chromosome.

Almost all of electric utility companies are planning to improve their management automation system, in order to meet the changing requirements of new liberalized energy market and to benefit from the innovations in information and communication technology (ICT or IT). Architectural design of the utility management automation (UMA) systems for their IT-enabling requires proper selection of IT choices for UMA system, which leads to multi-criteria decision-makings (MCDM). In response to this need, this paper presents a model-based architectural design-decision methodology. The system design problem is formulated first; then, the proposed design method is introduced, and implemented to one of the UMA functions–feeder reconfiguration function (FRF)– for a test distribution system. The results of the implementation are depicted, and comparatively discussed. The paper is concluded by going beyond the results and fair generalization of the discussed results; finally, the future under-study or under-review works are declared.

One of the most important issues that we face in controlling delayed systems and non-minimum phase systems is to fulfill objective orientations simultaneously and in the best way possible. In this paper proposing a new method, an objective orientation is presented for controlling multi-objective systems. The principles of this method is based an emotional temporal difference learning, and has a neuro-fuzzy structure. The proposal method, regarding the present conditions, the system action in the part and the controlling aims, can control the system in a way that these objectives are attain in the least amount of time and the best way. To clarify the issue and verify the proposed the method, three well known control examples which are hard to handle through classic methods are handled by means of the proposed method.

In this paper, a new Fuzzy Morphology (FM) based on the Generalized Dempster Shafer Theory (GDST) is proposed. At first, in order to clarify the similarity of definitions between Mathematical Morphology (MM) and Dempster Shafer Theory (DST), dilation and erosion morphological operations are studied from a different viewpoint. Then, based on this similarity, a FM based on the GDST is proposed. Unlike previous FM’s, proposed FM does not need any threshold to obtain final eroded or dilated set/image. The dilation and erosion operations are carried out independently but complementarily. The GDST based FM results in various eroded and dilated images in consecutive alpha-cuts, making a nested set of convex images, where each dilated image at a larger alpha-cut is a subset of the dilated image at a smaller alpha-cut. Dual statement applies to eroded images.

In today’s dynamic and uncertain environment responsiveness is one of the most important priorities and characteristics of manufacturing organizations. Responsiveness is a multidimensional concept and vague notion. For this reason we are often facing many problems when assessing it. Thus, in this paper we explain a knowledge-based methodology for the measurement and assessment of organizational responsiveness. At first, we explain responsiveness dimensions (Strategic Alignment, People, Process and Technology). In the next stage we will use the knowledge that is presented via "IF {fuzzy antecedents} THEN {fuzzy consequents} rules", to assess organizational responsiveness.

A novel intelligent neural optimizer with two objective functions is designed for electrical distribution systems. The presented method is faster than alternative optimization methods and is comparable with the most powerful and precise ones. This optimizer is much smaller than similar neural systems. In this work, two intelligent estimators are designed, a load flow program is coded, and a special modified heuristic optimization algorithm is developed and used too. The load pattern concept is used for training ANNs. Finally, the designed optimizer is tested on an example distribution system; simulation results are presented, and compared with similar systems.

In this paper a fuzzy expert system for predicting the performance of a switched reluctance motor has been developed. The design vector consists of design parameters, and output performance variables are efficiency and torque ripple. An accurate analysis program based on Improved Magnetic Equivalent Circuit (IMEC) method has been used to generate the input-output data. These input-output data is used to produce the initial fuzzy rules for predicting the performance of Switched Reluctance Motor (SRM). The initial set of fuzzy rules with triangular membership functions has been devised using a table look-up scheme. The initial fuzzy rules have been optimized to a set of fuzzy rules with Gaussian membership functions using gradient descent training scheme. The performance prediction results for a 6/8, 4kw, SR motor shows good agreement with the results obtained from IMEC method or Finite Element (FE) analysis. The developed fuzzy expert system can be used for fast prediction of motor performance in the optimal design process or on-line control schemes of SR motor.