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Figure 2 Structure of residual error generator based on CPRBF network.
By means of Takagi Sugeno fuzzy models, the dynamics of filtering error generator and the fuzzy RFDF system are constructed.
The structure of a residual error generator is shown in Figure 2, where x t) is the time series which can be observed of actual system, CPRBF is the residual error generator model trained using the dataset under normal state, x ^ ( t ) is the one-step prediction value of system, and e(t) is the output of residual error generator.
Step 5. Construct a residual error generator, and calculate the residual error according to the predicted output and the corresponding system output.
This error generator is positioned at the TRANS_PHY module output and modifies some data bits according to an insertion error rate configured in milliseconds.
Process of fault diagnosis based on CPRBF and residual error generator is shown in Figure 3. Figure 3 Process of fault detection.
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The oral response generator generates an error when the selected answer involves the use of a data not provided by the user simulator.
The stability condition is derived and the system is shown to satisfy the internal model principle, which assures that the control output tracks a class of reference commands without a steady-state error if the generator for the references is included in the stable closed-loop system.
The pitch angle controller consists of a PI controller equipped with an anti-windup circuit that generates a pitch angle reference from the generator speed error.
It consists of a pulse generator, an error detector and a multiplexer along with a conventional flip-flop.
In the trajectory generator module, the error signal is based on performance error, the error observed by the participant during each trial.
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