Therefore, performance approximations and bounds need to be applied.
This selection allows for a performance metric approximation given by (16). 2.
Figure 2 BER performance versus approximation parameter N (parallel TTCM, AvN Log-MAP, AWGN, K = 684).
Under faster manipulator performance, an approximation algorithm based on heuristics and a local search was shown to produce near-optimal harvest assignments.
These results show that optimisation of parlour performance and approximation to maximum labour efficiency should be addressed differently depending on the characteristics of the farm; in some cases, an improvement in management activities is necessary.
As a consequence, in case of rapid sampling, complicated process dynamics and/or high demands on closed-loop performance, satisfactory approximation of the control signal requires a very large number of forward shift operators, and leads to poorly numerically conditioned solutions and heavy computational load when implemented on-line.
The parameters of the numerical analysis for comparing the performance of approximations 1 and 2 are presented in Table 1. Figure 2a,b shows the results in 3D graphs.
Table 1 Parameter setting in numerical analysis for comparing the performance of approximations 1 and 2 Parameter Setting Number of channels (C) 12 Reserved channels (T) 4 μ H,μ BE,μ nRT 1 λ nRT 0.5 λ H 0.5×i, i=1,2,…,10 λ BE 0.5×i, i=1,2,…,10 Figure 2 CBP and CDP performance of system approximations 1 and 2 for a system with C=12, T=6, and λ nRT=0.5.(a) CBP and (b) CDP.
Furthermore, our analysis is based on a limited number of replicates per model and parameter combination (100 replicate datasets), making our estimate of performance a Monte Carlo approximation given finite computational resources.
We characterize the performance of sparse approximation applied to the estimation problem addressed herein in terms of the minimum number of states that need to be observed to achieve an accurate estimate of the cost-to-go function.
