Synthesis of Sparse Arrays in Presence of Coupling Effects Based on ANN and IWO

This work presents a novel optimization method which is based on artificial neural network (ANN) and Invasive Weed Optimization (IWO) for the pattern synthesis of coupled sparse arrays. The algorithm can optimize both the excitation and the placement of the array elements in presence of coupling effects. With the freedom of array element locations, we can take use of fewer elements to acquire more diverse patterns theoretically. One example is given for synthesizing the patterns of dipole arrays. Synthesis results show the effectiveness and advantages of the proposed method.