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A multilayer neural network structure for analog filtering

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2 Author(s)
Mehr, I. ; Crystal Semicond. Corp., Austin, TX, USA ; Sculley, T.L.

The design of analog filters has been a topic of research for many years, yielding a wide variety of techniques for addressing the problem. The work described here approaches this task from a neural network perspective to obtain some of the advantages of neural systems, such as a high tolerance to component imprecision and an ability to train or adapt high-order structures. Investigations of linear filter networks utilizing neural-like system topologies are presented, along with accompanying training algorithms and simulation results. Design of a reduced interconnect network in 2 μm CMOS is suggested, with simulations indicating its potential for implementing high order, self-programming analog filters at bandwidths above 70 MHz

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Circuits and Systems II: Analog and Digital Signal Processing, IEEE Transactions on  (Volume:43 ,  Issue: 8 )