Approximating a sine wave using neural network

Lets try to generalize a sine wave using neural network.  The NN will learn the data and will approximate. We will need atleast 20 hidden neurons for the NN to model properly.  For a NN to generalize to a non-linear output, it needs atleast 20 neurons in the hidden layer.

I used about 20 neurons in the hidden layer and trained for about 800 epocs below you see the result of the learning capability of the neural network.

Red –  Original Training data
Blue – NN output after learning

figure_out1

Neural network output after training

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Balakrishnan Prabhu

Mr. Balakrishnan Prabhu is the founder of Corpocrat magazine. He is also the founder of Best Citizenships (BC), assisting wealthy individuals with with global citizenship and residency programs in Europe. His other interests are Linux, Machine learning, Wordpress, etc. You can contact him here

  • Titas Nandi

    What is the architecture? Can I see it in more detail. Thanks.