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## Package org.encog.engine.network.activation

Interface Summary
ActivationFunction | This interface allows various activation functions to be used with the neural network.

Class Summary
ActivationBiPolar | BiPolar activation function.
ActivationBipolarSteepenedSigmoid | The bipolar sigmoid activation function is like the regular sigmoid activation function, except Bipolar sigmoid activation function.
ActivationClippedLinear | Linear activation function that bounds the output to [-1,+1].
ActivationCompetitive | An activation function that only allows a specified number, usually one, of the out-bound connection to win.
ActivationElliott | Computationally efficient alternative to ActivationSigmoid.
ActivationElliottSymmetric | Computationally efficient alternative to ActivationTANH.
ActivationGaussian | An activation function based on the Gaussian function.
ActivationLinear | The Linear layer is really not an activation function at all.
ActivationLOG | An activation function based on the logarithm function.
ActivationRamp | A ramp activation function.
ActivationSigmoid | The sigmoid activation function takes on a sigmoidal shape.
ActivationSIN | An activation function based on the sin function, with a double period.
ActivationSoftMax | The softmax activation function.
ActivationSteepenedSigmoid | The Steepened Sigmoid is an activation function typically used with NEAT.
ActivationStep | The step activation function is a very simple activation function.
ActivationTANH | The hyperbolic tangent activation function takes the curved shape of the hyperbolic tangent.

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