This section shows that the expectation value of the square error of the anticipated sensory input increases only linearly with the number of anticipation steps (Hoffmann and Möller, 2004). Let be the error of the feedforward output after a single step. is a vector with one component for each output component. We assume that the probability distribution of this error is independent of the input to the network. Thus, all errors are independent of each other. In addition, we assume that the error for each output component has zero mean and the same standard deviation .
On this basis, we compute the expectation value of the square error. The total error of the chain output is the sum of the errors of the outputs of each link. To illustrate this, think of each correct transformation at one link as a line in a ddimensional space, with d equal to the number of output components (figure C.1).

A line connects an input point with an output point (of the transformation). The error at link i can be drawn as an arrow at the end of a line (output point). This will result in a different starting point for the next line. If the error is small^{C.1} and the transformation function sufficiently smooth, we can approximate that the displacement of the starting point does not change the direction and length of the next line, which is the correct transformation at the new starting point. Thus, the displacement of the final point is the sum of the vectorial errors of each stage. Therefore, given l links, the total error E can be written as
E = .  (C.10) 
E^{2} = .  (C.11) 
E^{2} = + ,  (C.12) 
E^{2}  =  +  
=  .  (C.13) 