As shown in Fig. 1, a conventional ED only consists of a band-pass filter (BPF), a squaring operator, an integrator, and a decision device.
In particular, they came with an integral control structure containing both an actuator unit (corresponding to an integrator) and a controller/sensor unit.
For example, the TEM shown in Figure 1 consists of an input transconductance amplifier, a feedback 1-bit DAC, an integrator, and a hysteresis quantizer.
Since the controlled object includes an integrator, a type-2 control system can be obtained by using 1DOF PID controller.
The modulator consists of an integrator, an internal A/D converter or quantizer and a D/A converter (DAC) used in the feedback path.
To this end, we adjusted an implementation of the Rosenbrock solver [ 57], an integrator with an adaptive step size, to a very precise timing of the events.
The modulator of this converter consists of an integrator and a level-crossing sampler in a negative feedback loop.
It consists of a square-law device, followed by an integrator, sampler, and a decision mechanism.
Mathematically, a neural differentiator is an additive complement of an integrator, i.e., the response of a differentiator is complementary to that of an integrator with similar time constants (Fig. 5A).
Hence, can be viewed as the output of an integrator with a white noise input.
