How to find relation between N components and predict the value of any one component using the predicted relation?
I understand your task can be formulated as an imputation task, see the link below for further details. Also before you proceed look up the concepts of missing data. 

In order to fill in missing data you’ll have to build N distinct models using available observations. Each of N models will have N-1 inputs. 

Note that because more than one component can be missing your models should be able to handle the missing data. According to my knowledge neural networks cannot handle the missing data out of the box. Tree based models, such that random forests or GBMs are more suitable for this kind of tasks. 

http://www.stat.columbia.edu/~gelman/arm/missing.pdf