Neural Networks are very good approaches for robots. The main function of Neural Net is to model the interdependence between all the
features. Now this can be done manually by selecting possible combinations of
features between themselves upto a certain degree. But this approach has drawbacks:
- It is tedious to go about selecting features.
- It costs time and additional computer resources to calculate the values of the new features you have introduced.
- Since you cannot visualize data more than 3-D you cannot be absolutely sure that your selected
features are enough to model your problem.
Now if you use an NN, the NN will automatically select the combination of features (provided it has enough hidden nodes) by adjusting the weights of connections between and the
features and nodes. The main advantages of this approach are:
- You don't have to manually select the
- If data is still not fitting you can easily increase or decrease the number of nodes without needing to modify the whole network.
- Also it will be computationally efficient since you don't have to calculate values of
factors that don't matter to the problem.
Hope this is what you were looking for!