2001 · 21 citations · 1 references
Polynomial ApproximationEvolving Neural NetworkSigmoid Activation FunctionArtificial Neural NetworksEquivalent Digital CircuitEngineeringComputational NeuroscienceCellular Neural NetworkHardware AlgorithmComputer EngineeringDigital Hardware ImplementationComputer ScienceDigital Circuit DesignSigmoid FunctionBrain-like ComputingApproximation TheorySignal ProcessingNeurocomputers
In this paper we propose a polynomial approximation of the sigmoid activation function and its derivative used in artificial neural networks, and we describe the design of the equivalent digital circuit using a floating-point representation for numbers. The simulation of the circuit realized with CMOS technology AMS 0.35/spl mu/m under a frequency of 300 MHz shows the efficiency of the implementation.
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