If you are talking about the HSL/RGB, then 1 ANN is certainly best - do you have working code for this?
Generally, I would advise using a single ANN when data is correlated (i.e. have some inter dependence). The point of an ANN is to find relationships between input and output, so if there is a relationship between data (inter dependence) then they should be included in the same ANN.
How to set up the input/output as numbers in [0,1] range may need some thought (often scaling with other functions first - the objective is to have values that are roughly evenly distributed between 0 and 1). If 90% of your input values are between 0.95 and 1, then they should be rescaled somehow for example. Careful choice of these parameters will be very important. Basically you want them to be as independent as possible (Use Red, Green Blue, not Red, Yellow and Magenta), and evenly scaled between 0 and 1. Also, as I mentioned, a cyclic variable like Hue is better represented by 2 values Cos(H) and Sin(H). You are trying to give the ANN the best chance to 'see' relationships by forming the system with your best understanding of the system characteristics (mathematically: linearise, normalise, orthogonalise the problem as much as you can), then use the smallest number of hidden layers and nodes that works, and train for as few epochs as possible to avaid over-training.
The choice of hidden layers (number of layers and nodes in them), other parameters in the ANN (generally use defaults), being sure not to 'over-train' etc - these are all a bit of an art that comes with experience, this is why I suggested working on the RGB/HSL problem before trying to do it for your rabbit AI program.
Generally, I would advise using a single ANN when data is correlated (i.e. have some inter dependence). The point of an ANN is to find relationships between input and output, so if there is a relationship between data (inter dependence) then they should be included in the same ANN.
How to set up the input/output as numbers in [0,1] range may need some thought (often scaling with other functions first - the objective is to have values that are roughly evenly distributed between 0 and 1). If 90% of your input values are between 0.95 and 1, then they should be rescaled somehow for example. Careful choice of these parameters will be very important. Basically you want them to be as independent as possible (Use Red, Green Blue, not Red, Yellow and Magenta), and evenly scaled between 0 and 1. Also, as I mentioned, a cyclic variable like Hue is better represented by 2 values Cos(H) and Sin(H). You are trying to give the ANN the best chance to 'see' relationships by forming the system with your best understanding of the system characteristics (mathematically: linearise, normalise, orthogonalise the problem as much as you can), then use the smallest number of hidden layers and nodes that works, and train for as few epochs as possible to avaid over-training.
The choice of hidden layers (number of layers and nodes in them), other parameters in the ANN (generally use defaults), being sure not to 'over-train' etc - these are all a bit of an art that comes with experience, this is why I suggested working on the RGB/HSL problem before trying to do it for your rabbit AI program.