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Asked: 2026-05-20T08:58:02+00:00

I have implemented a multilayer perceptron to predict the sin of input vectors. The

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I have implemented a multilayer perceptron to predict the sin of input vectors. The vectors consist of four -1,0,1’s chosen at random and a bias set to 1. The network should predict the sin of sum of the vectors contents.

eg Input = <0,1,-1,0,1> Output = Sin(0+1+(-1)+0+1)

The problem I am having is that the network will never predict a negative value and many of the vectors’ sin values are negative. It predicts all positive or zero outputs perfectly. I am presuming that there is a problem with updating the weights, which are updated after every epoch. Has anyone encountered this problem with NN’s before? Any help at all would be great!!

note: The network has 5inputs,6hidden units in 1 hidden layer and 1 output.I am using a sigmoid function on the activations hidden and output layers, and have tried tonnes of learning rates (currently 0.1);

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  1. Editorial Team

    Being a long time since I looked into multilayer perceptrons hence take this with a grain of salt.

    I’d rescale your problem domain to the [0,1] domain instead of [-1,1]. If you take a look at the logistic function graph:

    enter image description here

    It generates values between [0,1]. I do not expect it to produce negative results. I might be wrong, tough.

    EDIT:

    You can actually extend the logistic function to your problem domain. Use the generalized logistic curve setting A and K parameters to the boundaries of your domain.

    Another option is the hyperbolic tangent, which goes from [-1,+1] and has no constants to set up.

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