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.
A neural network is presented for solving a general linear system using an approach that is similar to successive over relaxation (SOR). The network is trained to find the appropriate relaxation parameter. A derivation of the algorithm and its relation to the SOR algorithm is given. The performance of the standard SOR and Jacobi methods are compared with the neural network for two sample problems.
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