Heriberto Jose Delgado

A novel neural network combined with FDTD for the synthesis of a printed dipole antenna

A novel synthesis artificial neural network (SYNTHESIS-ANN) is combined with the finite-difference time-domain method. Practical applications are illustrated through the optimization of a dipole antenna input impedance. The ANN architecture utilizes a hetero-associative memory, which exploits a fault tolerant number representation of a neural network for input and output data. In addition, the number representation reveals significant insight into a new method of fault tolerant computing. A new randomization process for the synthesis of antenna geometrical parameters is presented. Additional work is required to investigate the potential of this new paradigm.

A novel neural network for the synthesis of antennas and microwave devices

A novel artificial neural network (SYNTHESIS-ANN) is presented, which has been designed for computationally intensive problems and applied to the optimization of antennas and microwave devices. The antenna example presented is optimized with respect to voltage standing-wave ratio, bandwidth, and frequency of operation. A simple microstrip transmission line problem is used to further describe the ANN effectiveness, in which microstrip line width is optimized with respect to line impedance. The ANNs exploit a unique number representation of input and output data in conjunction with a more standard neural network architecture. An ANN consisting of a heteroassociative memory provided a very efficient method of computing necessary geometrical values for the antenna when used in conjunction with a new randomization process. The number representation used provides significant insight into this new method of fault-tolerant computing. Further work is needed to evaluate the potential of this new paradigm.

Synthesis of Electromagnetic Devices with a Novel Neural Network

A novel Artificial Neural Network (ANN) is presented, which has been designed for computationally intensive problems, and applied to the optimization of electromagnetic devices such as antennas and microwave devices. The ANN exploits a unique number representation in conjunction with a more standard neural network architecture. An ANN consisting of hetero-associative memory provided a very efficient method of computing the necessary geometrical values for the devices, when used in conjunction with a new randomization process. The number representation used provides significant insight into this new method of fault-tolerant computing. Further work is needed to evaluate the potential of this new paradigm.