摘要：In this paper, a hybrid Fuzzy Neural Network (FNN) system for function approximation is presented. The proposed FNN can handle numeric and fuzzy inputs simultaneously. The numeric inputs are fuzzified by input nodes upon presentation to the network while the Fuzzy rule based knowledge is translated directly into network architecture. The connections between input to hidden nodes represent rule antecedents and hidden to output nodes represent rule consequents. All the connections are represented by Gaussian fuzzy sets. The method of activation spread in the network is based on a fuzzy mutual subsethood measure. Rule (hidden) node activations are computed as a fuzzy inner product. For a given numeric o fuzzy input, numeric outputs are computed using volume based defuzzification. A supervised learning procedure based on gradient descent is employed to train the network. The model has been tested on two different approximation problems: sine-cosine function approximation and Narazaki-Ralescu function and shows its natural capability of inference, function approximation, and classification.

摘要：In the process of multidisciplinary design optimization, there exits the calculation complexity problem due to frequently calling high fidelity system analysis models. The high fidelity system analysis models can be surrogated by approximate models. The sensitivity analysis and numerical noise filtering can be done easily by coupling approximate models to optimization. Approximate models can reduce the number of executions of the problem's simulation code during optimization, so the solution efficiency of the multidisciplinary design optimization problem can be improved. Most optimization methods are based on gradient. The gradients of the objective and constrain functions are gained easily. The gra- dient-based Kriging (GBK) approximate model can be constructed by using system response value and its gradients. The gradients can greatly improve prediction precision of system response. The hybrid optimization method is constructed by coupling GBK approximate models to gradient-based optimiza- tion methods. An aircraft aerodynamics shape optimization design example indicates that the methods of this paper can achieve good feasibility and validity.

摘要：Improving the efficiency of ship optimization is crucial for modem ship design. Compared with traditional methods, multidisciplinary design optimization （MDO） is a more promising approach. For this reason, Collaborative Optimization （CO） is discussed and analyzed in this paper. As one of the most frequently applied MDO methods, CO promotes autonomy of disciplines while providing a coordinating mechanism guaranteeing progress toward an optimum and maintaining interdisciplinary compatibility. However, there are some difficulties in applying the conventional CO method, such as difficulties in choosing an initial point and tremendous computational requirements. For the purpose of overcoming these problems, optimal Latin hypercube design and Radial basis function network were applied to CO. Optimal Latin hypercube design is a modified Latin Hypercube design. Radial basis function network approximates the optimization model, and is updated during the optimization process to improve accuracy. It is shown by examples that the computing efficiency and robustness of this CO method are higher than with the conventional CO method.

摘要：在设计优化中,确定性优化由于没有考虑输入量的不确定性,其优化结果可能不可靠(不安全),因此基于可靠性的设计优化(reliability-based design optimization,RBDO)得到关注。然而可靠性设计优化计算量大,尤其对于高维问题。基于此,提出一种新方法——改进拉丁超立方体取样(Latin hypercube sampling,LHS)方法,该方法可利用先前迭代步骤已用的取样点,从而降低计算量。其中可靠性指数通过基于漫射近似(diffuse approximation,DA)的一阶可靠性方法(first-order reliability method,FORM)计算得到。最后用两个数学实例验证该方法可以极大地降低RBDO问题的计算量。

摘要：提出了一种结合约束二次逼近优化(bound optimization by quadratic approximation,BOBYQA)搜索算法的理想点法对非支配解进行局部优化的混合多目标粒子群方法(local search with multiobjective particle swarm optimization,LSMOPSO),以提高多目标粒子群算法的收敛性能和非支配解集的精度与多样性。LSMOPSO算法使用拥挤距离选择领导粒子组成领导粒子集,并对其进行理想点局部搜索;分析比较了全局理想点和局部理想点对算法性能的影响,提出基于局部理想点的局部搜索策略;在粒子的设计空间的多个维度上引入均匀变异操作,降低算法陷入局部最优的可能。基本测试函数的求解结果表明,算法的收敛速度很快,而且搜索到的非支配解集的精度高、多样性好。

摘要：Two system optimization architectures are proposed for missile system preliminary design, taking into account aerodynamics, weights and sizing, propulsion and trajectory. approximation methods are investigated in order to reduce problem dimensionality and to improve the efficiency of optimization process.

摘要：Some local algorithms for the,preprocessing,of empirical data for both curves and surfaces are proposed so that the modified data,a polygon for curve data and a triangulation for surface data,can be used not only to approximate the given data with required accuracy,but also to preserve the shape and geometric and mathematical properties of the data such as convexity and monotonicity *** algorithms would produce small errors (O(h￣4)) if the initial data are accurate and reasonably distributed.

摘要：In this paper, we present an algorithm for reconstruction of B-spline surface such that it interpolates the four given bound- ary curves and simultaneously approximates some given inner points. The main idea of our method is： first, we construct an initial surface which interpolates the four given boundary curves; then, while keeping the boundary control points of the initial surface un- changed, we reposition the inner control points of the surface with energy optimization method. Examples show that our algorithm is practicable and effective.