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Hello,
I'm interested in neural network programming but I am a beginner.
Recently I wrote an example in C++ that I found in a book. It was a feedforward 3 layers network that learns the examples with the backpropagation algorithm. The aim of the network was to learn to "throw a stone": I taught it with some hundred numerical examples of shooting angles and speeds in output (calculated with the physics formulas) and time of flight and distance reached in input, repeated for some tens of thousands of epochs. After that, I ran the network: by putting distance and time of flight in input I obtained relatively good values of angle and speed in output (when tested, they lead to values of distance and time very near to those in input).
But then I tried to make another network with only the distance parameter in input, and I taught the net again with other examples (randomly chosen ). This try failed: I couldn't go beyond a very poor level of accuracy.
I have a faint idea of what the problem can be:I think the main issue is that there are many possible combinations of angle and speed that lead to the same distance, and maybe the network gets "confused". Maybe I simply made some mistake. Any explanation? How can I solve the problem? Do I need another kind of neural network?
Thank you
I'm interested in neural network programming but I am a beginner.
Recently I wrote an example in C++ that I found in a book. It was a feedforward 3 layers network that learns the examples with the backpropagation algorithm. The aim of the network was to learn to "throw a stone": I taught it with some hundred numerical examples of shooting angles and speeds in output (calculated with the physics formulas) and time of flight and distance reached in input, repeated for some tens of thousands of epochs. After that, I ran the network: by putting distance and time of flight in input I obtained relatively good values of angle and speed in output (when tested, they lead to values of distance and time very near to those in input).
But then I tried to make another network with only the distance parameter in input, and I taught the net again with other examples (randomly chosen ). This try failed: I couldn't go beyond a very poor level of accuracy.
I have a faint idea of what the problem can be:I think the main issue is that there are many possible combinations of angle and speed that lead to the same distance, and maybe the network gets "confused". Maybe I simply made some mistake. Any explanation? How can I solve the problem? Do I need another kind of neural network?
Thank you
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. The time is required to give it enough information to define them in terms of constants.
