A new reflector shaping technique to improve the beam scanning capability of hybrid antennas (i.e., a reflector system illuminated by a phased array) is presented. Errors between the desired scan directions and the outgoing rays’ directions in the scanning range are minimized by adjusting the reflector shape. This technique can be used to decrease the active element number of the feed array during the antenna scans, or reduce the gain loss caused by beam scanning when the number of active elements is given. An offset reflector antenna system is designed and analyzed, through which the validity of the technique is demonstrated.

The application of the new nonlinear optimization algorithms to arrays design is demonstrated by the low-side-lobe pattern synthesis of conformal arrays. By adopting the information of array element realized gain patterns (RGP’s), we formulate synthesis problems as nonlinearly constrained optimization problems, and propose a new direct method to solve them. The technique allows one to find a new direct method to solve them. The technique allows one to find a set of array coefficients that yield a pattern meeting a specified side-lobe level and achieving the maximum directivity, if such a set of array coefficients exists. If the side-lobe specifications cannot be met with the given array, the technique will result in a set of coefficients that yield a pattern meeting the best attainable side-lobe level and having directivity as high as possible. Also, simplified synthesis problems for an axial dipole array in an infinite, perfectly conducting cylinder are discussed, and numerical synthesis results are given. Our synthesis technique works for general array patterns.

In this paper, a new gradient-based neural network is constructed on the basis of the duality theory, optimization theory, convex analysis theory, Lyapunov stability theory, and LaSalle invariance principle to solve linear and quadratic programming problems. In particular, a new function F(x, y) is introduced into the energy function E(x, y) such that the function E(x, y) is convex and differentiable, and the resulting network is more efficient. This network involves all the relevant necessary and sufficient optimality conditions for convex quadratic programming problems. For linear programming (LP) and quadratic programming (QP) problems with unique and infinite number of solutions, we have proven strictly that for any initial point, every trajectory of the neural network converges to an optimal solution of the QP and its dual problem. The proposed network is different from the existing networks which use the penalty method or Lagrange method, and the inequality (including nonnegativity) constraints are properly handled. The theory of the proposed network is rigorous and the performance is much better. The simulation results also showthat the proposed neural network is feasible and efficient.

e new version of the Price’s algorithm given in Brachetti et al. [J. Global Optim. 10, 165–184 (1997)]. Its distinguishing features include: (1) The number-theoretic method is applied to generate the initial population so that the points in the initial population are uniformly scattered, and therefore the algorithm could explore uniformly the region of interest at the initial iteration; (2) The simplified quadratic approximation with the three best points is employed to improve the local search ability and the accuracy of the minimum function value, and to reduce greatly the computational overhead of the algorithm. Two sets of experiments are carried out to illustrate the efficiency of the number-theoretic method and the simplified quadratic model separately. The proposed algorithm has also been compared with the original one by solving a wide set of benchmark problems. Numerical results show that the proposed algorithm requires a smaller number of function evaluations and, in many cases, yields a smaller or more accurate minimum function value. The algorithm can also be used to deal with the medium size optimization problems.

A new method to improve the gain of patch antenna with metamaterial composed of ring aperture lattice is presented. The influences of the number of metamaterial layers and a comparison of electromagnetic characteristics between the conventional patch antenna and the new metamaterial patch antenna are studied by using numerical simulation method. Then, a patch antenna with the metamaterial is fabricated and measured. The simulation and experimental results show that this method is effective and this structure can realize congregating the radiation energy, thus the gain of the antenna with metamaterial can greatly increase when compared with the conventional one.