Neural networks for signal processing




Bhattacharya, Dipankar

Journal Title

Journal ISSN

Volume Title



The application of neural networks in the area of signal processing is examined. Two major areas are identified and suitable neural networks are developed. In the first area, neural networks are used as a tool for the design of digital filters. In the second area, neural networks are used for processing bathymetric data. The field of artificial neural networks is first introduced with an emphasis on Hopfield networks. The optimizing capabilities of such networks are noted. Based on these networks, a feedback neural network is developed for the design of 1-D finite-duration impulse response (FIR) filters on the basis of given amplitude responses. A suitable cost function is formulated first and an associated network is developed. This work is then extended to the design of two more networks for the design of FIR filters based on given amplitude and phase responses and prescribed specifications. The idea is extended to the design of 2-D FIR filters. Two networks are presented for designing 2-D FIR filters on the basis of a given amplitude response and prescribed specifications. The design of 1-D infinite-duration impulse response (IIR) filters is studied next and two networks are developed. The first one is to design filters with prescribed specifications in the magnitude-squared domain. The other network designs IIR filters for a given frequency response. A network for designing equiripple 1-D FIR filters based on the weighted least-squares technique is presented next. A new updating algorithm is developed for this network. Two different neural networks are proposed for classifying lidar waveforms into various categories. A single-layer network is developed for classifying lidar waveforms representing milt of varied densities. A fast version of the supervised learning algorithm is presented. A threshold term is also introduced in the recall phase to give the user flexibility to accept or reject any waveform. A two-stage, multi-layer network is presented next which uses waveform characteristics to assign a signature number to the waveform. This network extracts various ocean parameters from the waveforms as well. The issue of implementing the feedback neural network is addressed next. Basic building blocks for implementing such networks are identified and a network is constructed from circuits existing in the literature. The network is simulated in Cadence using 0.8 μ BICMOS technology. The results show that these networks have a high potential to be implemented in analog VLSI for real-time signal processing.



Neural networks (Computer science), Signal processing