Abstract
A method for designing a multiplierless multilayer feedforward neural network (MFNN) suitable for continuous input-output mapping is presented. When tested with noisy vectors, the network can retain a similar recall accuracy as the corresponding MFNN with continuous weights. The advantages of the design method include faster computational speed and reduced digital hardware cost.
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