A NEURAL-NETWORK SPECTRUM ANALYZER

The Fourier Series Neural Network (FSNN) is used as a Neural Network S pectrum Analyzer (NNSA) to gain the advantages of computational parall elism, self-learning intelligence and the capability of modeling compl ex systems with multiple variables. A comprehensive comparison study b etween the NNSA and a traditional spectrum analyzer based on the Fast Fourier Transformation (FFT) was conducted to evaluate the performance of the NNSA when used for transfer function identification. This eval uation was carried out using computer simulations and experimentation in which a mechatronic robot end-effector was used as the plant to be identified using the two methods. The identification results described in this paper demonstrate that the NNSA is able to approach the same model accuracy as FFT.