Conceptual design optimization of spacecraft systems is a complex and multidisciplinary process. In this case evaluation of the objective functions relies heavily on running iterative simulation models and analysis codes between various subsystems (such as structures, payload, electrical power supply, attitude determination and control, communication, command and data handling). The conventional sequential optimization approaches to such a complex design problem is time consuming and does not guarantee to achieve the best compromise among the various competing coupled subsystems, and may even lead to non-optimal design. In addition, the design search space can be multi-modal, non-convex with multiple local minima and hence it is time consuming or difficult to rapidly evaluate trade-offs between various subsystems (disciplines). To address these issues, in this paper an efficient surrogate (response surface) model-based multidisciplinary spacecraft systems design optimization technique with discrete and continuous design variables is presented. The methodology is based on the utilization of genetic algorithms (GA) for both system level and discipline level as an optimizer. Surrogate-modeling as an efficient tool is also used to decrease computational cost in discipline (subsystem) level within a collaborative optimization (CO) framework. Results obtained in this study show that the method introduced in this paper provides an effective way of improving computational efficiency of a complex space system design such as conceptual design optimization of a spacecraft.

least square matching (LSM) is one of the most accurate image matching methods in photogrammetry and remote sensing. The main disadvantage of the LSM is its high computational complexity due to large size of observation equations. To address this problem, in this paper a novel method, called fast least square matching (FLSM) is being presented. The main idea of the proposed FLSM is decreasing the size of the observation equations to improve the efficiency of the matching process. For this purpose, the pixels in the matching window are ordered using a special robustness measure. Then, a specific percent of the pixels with the highest robustness is selected for matching process. The phase congruency and entropy measures are used to compute the proposed robustness measure. The proposed FLSM method was successfully applied to match various synthetic and real image pairs, and the results demonstrate its capability to increase matching efficiency. The matching results show that the proposed FLSM method is three times faster than standard LSM method.

The Moving least square (MLS) interpolation method is proposed for approximation of adaptive fuzzy controller parameters for two degrees of freedom suspension system and each one has two inputs, one output with twenty-five linguistic fuzzy IF-THEN rules. Fuzzy systems are designed by using five Gaussian membership functions for each input, product inference engine, singleton fuzzifier and center average defuzzifier. The constructed fuzzy systems is composed with adaptation rules. For this purpose, Lyapunove approach is implemented for stability of the adaptation rules. The Gravity Search Algorithm (GSA) is implemented for achieve the optimum controller parameters. The relative displacement between sprung mass and tire and the body acceleration are two objective functions used in the optimization algorithm. Since, choose the suitable controller coefficients are important and when the parameter of the system change, Optimum coefficients of the controller will also change. In order to solve this obstacle, the MLS predictive model is purposed that is interpolation method based on a radius of the neighborhood, a basis function and a weight function for points of interest. Finally online model is implemented on the two degrees of freedom suspension system and results compared with the offline optimal systems.

While the Hammerstein expression is computationally attractive for modeling of nonlinear systems, the optimal calculation of filter coefficients is practically cumbersome. A proper solution to this problem is the use of adaptive algorithms. In this paper, the Hammerstein Normalized LMS algorithm is proposed by deriving the corresponding time varying optimal step-size parameter in a closed form. The convergence behavior of this algorithm is inspected using computer simulations. The results show that the proposed algorithm achieves a faster convergence speed compared to the Hammerstein LMS algorithm, practically at the cost of an acceptable increase in computations.

In this research, a linear combination of Moving least square (MLS) and local radial basis functions (LRBFs) is considered within the framework of the meshless method to solve the two-dimensional hyperbolic telegraph equation. Besides, the differential quadrature method (DQM) is employed to discretize temporal derivatives. Furthermore, a control parameter is introduced and optimized to achieve minimum errors via an experimental approach. Illustrative examples are provided to demonstrate the applicability and efficiency of the method. The results prove the superiority of this method over using MLS and LRBF individually.

The Mixed least squares Meshfree (MDLSM) method has shown its appropriate efficiency for solving Partial Differential Equations (PDEs) governing the engineering problems. The method is based on the minimizing the residual functional. The residual functional is defined as a summation of the weighted residuals on the governing PDEs and the boundaries. The Moving least squares (MLS) is usually applied in the MDLSM method for constructing the shape functions. Although the required consistency and compatibility for the approximation function is satisfied by the MLS, the method loss its appropriate efficiency when the nodal points cluster too much. In the current study, the mentioned drawback is overcome using the novel approximation function called Mapped Moving least squares (MMLS). In this approach, the cluster of closed nodal points maps to standard nodal distribution. Then the approximation function and its derivatives compute noting the some consideration. The efficiency of suggested MMLS for overcoming the drawback of MLS is evaluated by approximating the mathematical function. The obtained results show the ability of suggested MMLS method to solve the drawback. The suggested approximation function is applied in MDLSM method, and used for solving the Burgers equations. Obtained results approve the efficiency of suggested method.

In seepage problems, the coefficients of permeability in Laplace equation are usually assumed to be constant vs. both space and time; but in reality these coefficients are variable. In this study, the effect of material deformation due to external loads (consolidation) and variation of head in the consolidation process are considered. For the first case, formulation of kx and ky can be defined by a second order binominal equation in order to take into account the material changes due to volume changes. For the second case, kx and ky can be defined as a function of unknown total head. The solution of the resulting non-linear differential equation is found using the least square Finite Element formulation. In order to increase the accuracy of the solution, eight nodal (isoperimetric) elements were obtained. This method was used satisfactorily to solve several seepage problems and to examine the accuracy and convergence of the results. The effect of a variable coefficient of permeability may not be significant on small dams, but as the height of the dam increases, the effect becomes more considerable. It is believed that a variable permeability analysis such as the one described in this paper should be taken into account.

The conventional fuzzy least-squares time series models show undesirable performance when the fuzzy data set involves the outliers. By introducing a strategy to detect the outliers, this paper introduced a method for reducing the influence of outliers on the future predictions. For this purpose, according to the weighted square distance error, an estimation procedure was suggested for determining the exact coefficients in the presence of outliers. The parameters of the fuzzy time series model were then estimated using an iterative algorithm. In order to identify the potential outliers of the fuzzy data, a  quality control chart was employed based on the center of gravity criterion of fuzzy data. The defuzzification method was also employed to examine the performance of the proposed method via some  scatter plots. Several common goodness-of-fit criteria used in traditional time series models were also extended to compare the performance of the proposed fuzzy time series method to an existing method. The effectiveness of the proposed method was illustrated through two numerical examples including a simulation study. The results clearly indicated that the proposed model performs well in terms of the both scatter plot criteria and goodness-of-fit evaluations in cases where the potential outliers exist among the fuzzy data.

A meshless approach, collocation discrete least square (CDLS) method, is extended in this paper, for solving elasticity problems. In the present CDLS method, the problem domain is discretized by distributed field nodes. The field nodes are used to construct the trial functions. The Moving least-squares interpolant is employed to construct the trial functions. Some collocation points that are independent of the field nodes are used to form the total residuals of the problem. The least-squares technique is used to obtain the solution of the problem by minimizing the summation of the residuals for the collocation points. The final stiffness matrix is symmetric and therefore can be solved via efficient solvers. The boundary conditions are easily enforced by the penalty method. The present method does not require any mesh so it is a truly meshless method. Numerical examples are studied in detail, which show that the present method is stable and possesses good accuracy, high convergence rate and high efficiency.