# Is it possible to train a neural network with 3 inputs and 12 outputs?

The selection of experimental data includes a set of vectors of different dimensions. The input is a 3-dimensional vector, and the output is a 12-dimensional vector. The sample size is 120 pairs of input 3-dimensional and output 12-dimensional vectors.

Is it possible to train such a neural network (in MATLAB)? Which structure of the neural network is best suited for this?

• Hello, could you provide an overview of the data and/or problem you're trying to solve? Without such information I can not give you a full answer. – razvanc92 Jan 17 '20 at 12:51
• Hello, I do direct kinematics of the robot. The input 3-dimensional vector consists of the rotation angles of the corresponding drive. The working body of the robot is a triangular platform. As the output, I record the coordinates of the vertices of the triangle in the base coordinate system (we get three 3-dimensional vectors = only 9 coordinates) and three angular velocities along the x, y and z axes. In total, a 12-dimensional output vector is obtained. – dtn Jan 17 '20 at 13:09
• I need to approximate data using a neural network, i.e. train it when applying the appropriate 3-dimensional vector to produce the corresponding 12-dimensional vector. I have a training database. – dtn Jan 17 '20 at 13:10
• Please see my adjusted starting communication. – dtn Jan 17 '20 at 18:07

There is nothing stopping you, you can setup Dense Neural Networks to have any size inputs or outputs (simple proof is to imagine a single layer NN with no activation is just a linear transform and given input dim $$n$$ and output dim $$m$$, it's just a matrix of $$n$$ x $$m$$, trivially this works though with any number of hidden layers)