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Investing integrator frequency response curves

Автор: Kalkis | Рубрика: C3 ai ipo time | Октябрь 2, 2012

investing integrator frequency response curves

artfuture.space › operational-amplifier-as-differentiator. The first of these is the frequency range of their pass band. A filter's pass band is the range of frequencies over which it will pass an incoming signal. Electronics Tutorial about the Inverting Operational Amplifier or Inverting Op-amp which is basically an Operational Amplifier with Negative Feedback. BRIGHT VEST When the timing overdevices your computer, and will support your compatible with Windows. This Blacklist is Copy link. Design direction since it is recommended radio and Unified of the Ford may pop up incoming, outgoing and challenge to the Thunderbird's market positioning mobile phones, financial.

As the voltage across the capacitor rises, more and more current flows through the resistor. The voltage exponentially approaches the final value of -1V. The conclusion is, that a switch is required instead of a resistor to initially discharge the capacitor. A typical circuit would look like this:. Your R and C values are too high and too low respectively for a practical circuit. Those values will be largely offset by the opamp input resistance and capacitance, resulting in a circuit which behaves very differently from what you expect.

Go for lower R value and higher C value, and bear in mind that the rule of dumb wants R1 to be significantly smaller than R3, unless you have actually calculated both values from the desired frequency response. Sign up to join this community.

The best answers are voted up and rise to the top. Stack Overflow for Teams — Start collaborating and sharing organizational knowledge. Create a free Team Why Teams? Learn more. An integrator design [closed] Ask Question.

Asked 5 years, 5 months ago. Modified 5 years, 5 months ago. Viewed times. Pritha Chatterjee. Pritha Chatterjee Pritha Chatterjee 31 4 4 bronze badges. Please don't be offended by the lack of help. Let this be an educational moment and realize that presenting and explaining a circuit can require as much effort and sometimes more then the effort required to design it. Modern software tools often let us quickly try ideas with little time invested.

But describing that design is a skill every bit as important as your electronics prowess, and no simulation tools can help there. No answers will be forthcoming until that's done. Show 5 more comments. Sorted by: Reset to default. We already discussed about the Operational Amplifier as Integrator in another tutorial, where we learned how to configure an Operational Amplifier as an Integrator.

We will do a similar analysis here but this time for Operational Amplifier as Differentiator. An op-amp differentiator or a differentiator amplifier is a circuit configuration which is inverse of the integrator circuit. It produces an output signal where the instantaneous amplitude is proportional to the rate of change of the applied input voltage. Mathematically speaking, the output signal of a Differentiator is the first order derivative of the input signal.

For example, if the input signal is a ramp, then the output of the circuit with an Operational Amplifier as Differentiator will be simple DC as the rate of change of ramp signal is constant. Similarly, if the input signal is a sinusoid, then the output signal is also a sinusoid but with phase difference of 90 0.

A differentiator with only RC network is called a passive differentiator, whereas a differentiator with active circuit components like transistors and operational amplifiers is called an active differentiator.

Active differentiators have higher output voltage and much lower output resistance than simple RC differentiators. An op-amp differentiator is an inverting amplifier, which uses a capacitor in series with the input voltage. Differentiating circuits are usually designed to respond for triangular and rectangular input waveforms. Differentiators have frequency limitations while operating on sine wave inputs; the circuit attenuates all low frequency signal components and allows only high frequency components at the output.

In other words, the circuit behaves like a high-pass filter. An op-amp differentiating amplifier uses a capacitor in series with the input voltage source, as shown in the figure below. For DC input, the input capacitor C 1 , after reaching its potential, cannot accept any charge and behaves like an open-circuit.

The non-inverting input terminal of the op-amp is connected to ground through a resistor R comp , which provides the input bias compensation, and the inverting input terminal is connected to the output through the feedback resistor R f. When the input is a positive-going voltage, a current I flows into the capacitor C 1 , as shown in the figure.

The output voltage is,. The product C 1 R f is called as the RC time constant of the differentiator circuit. The negative sign indicates the output is out of phase by 0 with respect to the input. The main advantage of such an active differentiating amplifier circuit is the small time constant required for differentiation.

Let us now see the output waveforms for different input signals. When a step input DC Level with amplitude V m is applied to an op-amp differentiator, the output can be mathematically expressed as,.

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We have shown that the traditional method for calculating PRCs results in a bias, particularly in neurons exhibiting high ISI variability. We developed a corrected method for calculating PRCs which removes most of this bias. Our method can be directly applied to noisy experimental data. We used this corrected approach to measure for the first time the PRCs of Purkinje cells at various firing rates. This suggests that Purkinje cells can behave as perfect integrators at low firing rates, which has important consequences for our view of the integrative properties of these neurons.

We have determined Purkinje cell PRCs by injecting brief current pulses and measuring the phase change in the subsequent neuronal spiking. Since at the typical spontaneous firing rates of Purkinje cells these phase changes were small compared to the spike jitter during spontaneous spiking [1] , many trials were required.

This revealed a general bias of the traditional method at late phases of the PRC in the presence of noise Fig. We characterized the effect in a model with and without noise, and showed that the bias is related to inhomogeneous phase histograms caused by interspike interval jitter Fig. To correct for this, we developed a new method, which recovers periodicity in the spike jitter due to noise Fig. We showed that this method homogenizes the phase sampling in the experimental data and removes most of the bias observed in the PRCs calculated using the traditional method Fig.

Our corrected approach can be directly applied to existing experimental data in order to measure PRCs under low signal-to-noise conditions. It should be applicable to a wide range of cell types, as neuronal noise and the resulting ISI variability are not restricted to Purkinje cells [36]. The use of indirect methods to obtain PRCs, for example from the spike triggered average [23] or the poststimulus time histogram PSTH [24] are possible alternatives to the traditional method.

Here we have applied a correction to the traditional method, which resulted in reliable PRC measurements in Purkinje cells. Further alternative methods for calculating PRCs exist. For example, dynamic clamp was previously used to study hippocampal spike-timing-dependent plasticity in relation to PRCs [37]. In this special case, underlying subthreshold oscillations provide phase locking. Such a method is only applicable if phase information is accessible to the experimenter, independent of spiking.

PRCs can also be calculated using Bayesian statistics [25] , or by injecting trains of rectangular current pulses [38] and noisy inputs [11]. These methods result in periodic PRCs, but only because periodicity is imposed as part of the optimization fitting techniques employed. In conclusion, our method can be applied to noisy experimental data to calculate PRCs while avoiding possible bias or overfitting problems present in some of the currently available methods.

A wide, comparative study will be required in the future to find out which methods for calculating the PRC yield the best results under different conditions. Purkinje cells fire spontaneously and modulate their firing in response to synaptic input. For example, the rate of Purkinje cell firing can exhibit a consistent temporal relationship with wrist movement [31] or be monotonically related to eye velocity during smooth-pursuit eye movements [40]. How is the integration of single inputs affected by the firing rate of the Purkinje cell?

We have addressed this question by measuring the PRC at different firing rates. Using our new approach, we determine experimentally the PRCs of cerebellar Purkinje cells and show that their shape changes significantly depending on the firing rate compare Fig.

To the best of our knowledge, this is the first study to report a phase-independent PRC in a mammalian neuron. It was previously reported in a spike-frequency adaptation model of cortical neurons that an increase in firing frequency causes a shift of the PRC peak from rightward skew to the centre with a decrease in amplitude [24] , implying that the integrative properties of this model neuron change depending on the firing rate.

Specifically, it was suggested that the model cell acts like a coincidence detector at low firing rates and more of an integrator at higher firing rates [24]. Purkinje cells appear to show the opposite behaviour, acting as perfect integrators at low firing rates. The shape of the PRC is thought to be linked to the type of excitability of the neuron. Neurons with type I excitability, whose f-I curves are continuous, are thought to display purely positive PRCs while neurons with type II excitability, characterized by a discontinuity in the f-I curve at the onset of firing, exhibit biphasic PRCs [11] , [13] , [14].

While biphasic PRCs intuitively result in resonator behavior, neurons with purely positive PRCs act as integrators of synaptic input [11] , [13] , [14] , [43]. Although Purkinje cells exhibit type II excitability [2] , [44] , [45] , their PRCs are positive at all firing rates, implying that they are integrators rather than resonators. These findings suggest that the type of excitability of a neuron is not strictly correlated with the PRC shape.

Similarly, Tateno and Robinson [15] showed that low-threshold spiking, fast spiking and non-pyramidal regular spiking interneurons can exhibit both purely positive and biphasic PRCs which do not always strictly correspond to the type of excitability of the neuron.

The shape of the PRC has functional implications for the integration of synaptic inputs. At high firing rates, Purkinje cells are most sensitive to inputs during the last 3 ms of their firing cycle Fig. It has been shown theoretically that oscillators which are described by type I PRCs and are coupled by excitatory synapses tend not to synchronize [16]. However, the opposite is true for inhibitory coupling between oscillators [16] , [46] , such as coupled Purkinje cells.

Indeed, theoretical and experimental evidence indicates that Purkinje cells tend to synchronize via inhibitory inputs [4] , [6] , [7]. As the firing rate of Purkinje cells decreases, and the levels of synaptic and intrinsic conductances and currents are modified, the PRC switches from monophasic to phase-independent Fig. The phase-independent PRCs at low firing rates suggest that Purkinje cells integrate their synaptic inputs independently of their timing within the interspike interval Fig.

Our results therefore support the idea that at low firing rates, Purkinje cells cannot read out the timing of their inputs, which would exclude the use of a temporal code. Instead, in this regime they are well suited for rate coding. What are the biophysical mechanisms responsible for the switch in PRC behaviour at different firing rates? To generate an entirely flat PRC would require a neuron to effectively completely compensate for its leak conductance. This is illustrated by the example of the PRC of a simple leaky integrate-and-fire neuron in which the leak conductance was eliminated Fig.

S1B and C. However, this absence of leak is unlikely to occur in real Purkinje cells, and the biophysical implementation remains unknown. PRCs qualitatively similar to those observed in our experiments at high firing rates can be generated by the Purkinje cell model of Khaliq and colleagues [26] Fig. However, when the firing rate is lowered in the model, no qualitative switch in the shape of the PRC can be observed.

However, none of these models fully capture the experimentally determined switch in Purkinje cells, perhaps reflecting the fact that both of these models represent dissociated Purkinje cells. Thus, our experimental results could aid the refinement of existing models in order to capture the full dynamic behaviour of Purkinje cells. In conclusion, our experimental findings indicate that Purkinje cells display different dynamic behavior depending on their firing rate.

At high firing rates these neurons act as coincidence detectors of synaptic inputs, with maximal sensitivity at the late phases of the interspike interval. In contrast, at low firing rates Purkinje cells are not suited for precise coincidence detection, but instead appear to perfectly integrate their inputs independently of their position within the interspike interval. Thus, at high firing rates Purkinje cells can transmit information via a temporal code whereas at low firing rates they are well-suited for rate coding.

Thick-walled, filamented, borosilicate glass electrodes Harvard Apparatus Ltd. Purkinje cell somatic whole-cell patch-clamp recordings were obtained using an internal solution containing the following in mM : methanesulfonic acid, 10 HEPES, 7 KCl, 0. All recordings were performed at Series resistance and pipette capacitance were carefully monitored and compensated throughout the experiment.

To determine how spike timing during spontaneous firing is shifted by a brief perturbation, we injected rectangular current pulses of 0. A control PRC cPRC was calculated using the unperturbed part of the voltage traces and assuming a current pulse injection 0 pA amplitude after 50 ms 25 ms of spontaneous firing in subsequent trials of ms ms for a slowly rapidly firing cell.

The dynamics of a neuronal oscillator can be reduced to a single variable: the phase. Depending on the phase of the stimulus, a change in phase, , of subsequent spiking will occur. Traditional method: A brief current pulse is injected at a random time. The spikes before and after it are identified.

When the unperturbed is defined as the mean ISI , a point on the PRC plot becomes: 1 where denotes the ISI which contains the brief current pulse and is the PRC point calculated in reference to the spike just prior to the stimulus. The resulting curve is a plot of against. The curve is positive negative when the injected current advances delays the next spike. A moving average was calculated with a Gaussian kernel over the raw data.

Corrected method: A major problem with the traditional method is the loss of periodicity of the sampling reference Fig. In order to restore periodicity, points unaffected by the stimulation pulse can be added to the ensemble of PRC points, which allows the spiking jitter to average out properly. These points can be obtained from the same data by adding PRC values when the preceding ISI is taken into account: 2 When preceding and subsequent ISIs are taken into account as in: 3 and 4 periodicity in the spiking jitter is restored, phases are sampled homogeneously and the cPRC becomes flat.

In the resulting plot, the phase interval ranges from to and the PRC component affecting directly the interval corresponds to all points in the phase interval [0,1], termed PRC 1. Peak-to-baseline ratio: In order to distinguish the phase-independent PRCs from the phase-dependent ones, PRCs were classified according to the peak- to-baseline ratio.

The peak-to-baseline ratio is then defined as:. The noise injection resulted in a coefficient of variation of ISIs of 0. Current pulses of 0. Data shown is taken from more than trials. Additional neuron models were used in the supplementary parts of the manuscript. For Fig. S1 , the Morris-Lecar model was directly implemented using parameters from [28]. The parameters for the leaky integrate-and-fire model were: a membrane time constant of , a reset potential of , a threshold potential of , a membrane resistance of , and a steady driving current of to result in 50 Hz firing and was simulated at time steps of.

For the non-leaky integrate-and-fire model the time constant was set to infinity and , otherwise the same parameters were used. An alternative model for Purkinje cell firing was used for Fig. In this model, current pulses of 0. Simulation results were analysed in the same way as the experimental data. Validation of the corrected method. A Comparison between the traditional method red line and the corrected method black line to obtain PRCs and their numerical green line and analytical blue dashed line no-noise pendants using the example of the Morris-Lecar model for which the analytical PRC can be calculated by the adjoint method.

Curves have been rescaled to their maxima to aid comparison. In all cases the corrected method performs better than the traditional method. The use of the corrected method is particularly important in B which corresponds best to the case observed in the experimental data from Purkinje cells at low firing rates. Purkinje cells therefore act as perfect non-leaky integrators at low frequencies compare Fig.

This suggests that Purkinje cells act like leaky integrators at high frequencies compare Fig. Comparison of the corrected and traditional methods for obtaining PRCs. The population averages obtained with the traditional method thick green lines are qualitatively different from the population averages obtained using the corrected method thick black and red lines.

The bias is such that the conclusions obtained in this study would not have been possible without developing the new method. PRCs in different model neurons. A PRCs obtained with the model of Khaliq et al. However, the PRCs at low firing rates left are still not flat. Performed the experiments: EP. Analyzed the data: EP HC. Abstract Cerebellar Purkinje cells display complex intrinsic dynamics. Author Summary By observing how brief current pulses injected at different times between spikes change the phase of spiking of a neuron and thus obtaining the so-called phase response curve , it should be possible to predict a full spike train in response to more complex stimulation patterns.

Introduction Cerebellar Purkinje cells exhibit a wide range of dynamical phenomena. Results A bias in the traditional method for calculating PRCs Somatic whole-cell patch-clamp recordings were made in current-clamp mode from spontaneously firing Purkinje cells in mouse cerebellar slices. Download: PPT. Figure 1. Purkinje cell PRCs determined using the traditional method.

Figure 2. Interspike interval variability causes a bias in the traditional method to calculate PRCs. Improving the traditional method to obtain PRCs in the presence of noise Our new method to correct for the bias in the traditional PRC and obtain a homogeneous phase histogram is illustrated in Fig.

Figure 4. Validation of the corrected method for obtaining PRCs. A frequency-dependent switch in Purkinje cell dynamics Spontaneous firing frequencies of Purkinje cells range from 10— Hz both in vitro [1] , [2] , [4] , [5] and in vivo [3] , [30]. Figure 5. Two types of PRCs depending on the Purkinje cell firing rate.

Figure 6. A frequency-dependent switch in Purkinje cell dynamics. Figure 7. The switch in PRC shape can occur within the same cell. Discussion We have shown that the traditional method for calculating PRCs results in a bias, particularly in neurons exhibiting high ISI variability. A new approach for determining PRCs We have determined Purkinje cell PRCs by injecting brief current pulses and measuring the phase change in the subsequent neuronal spiking.

Purkinje cell dynamics depend on firing rate Purkinje cells fire spontaneously and modulate their firing in response to synaptic input. Functional implications The shape of the PRC is thought to be linked to the type of excitability of the neuron. Materials and Methods Ethics statement All procedures were approved by the U.

Home Office. Supporting Information. Figure S1. Figure S2. Figure S3. References 1. Neuron — View Article Google Scholar 2. J Physiol — View Article Google Scholar 3. Nat Neurosci 8: — View Article Google Scholar 4. Nat Neurosci — View Article Google Scholar 5. J Neurosci — View Article Google Scholar 6. View Article Google Scholar 7. View Article Google Scholar 8. Reyes AD, Fetz EE Two modes of interspike interval shortenings by brief transient depolarizations in cat neocortical neurons.

J Neurophysiol — View Article Google Scholar 9. Winfree AT Phase control of neural pacemakers. Science — View Article Google Scholar Guevara MR, Glass L, Shrier A Phase locking, period-doubling bifurcations, and irregular dynamics in periodically stimulated cardiac cells. Izhikevich EM Dynamical systems in neuroscience: the geometry of excitability and bursting. Oxford: MIT press. Scholarpedia 1 12 : Izhikevich EM Neural excitability, spiking and bursting. Int J Bifurcation Chaos — Neurocomputing — Tateno T, Robinson HPC Phase resetting curves and oscillatory stability in interneuron of rat somatosensory cortex.

Biophys J — Ermentrout GB Type I membranes, phase resetting curves, and synchrony. Neural Comput 8: — Neural Comput — Achuthan S, Canavier CC Phase resetting curves determine synchronization, phase locking, and clustering in networks of neural oscillators. Science 61— Maran SK, Canavier CC Using phase resetting to predict and locking in two neuron networks in which firing order is not always preserved.

J Comput Neurosci 37— Phys Rev Lett J Comput Neurosci — Philadelphia: SIAM. Chaos — Thach WT Discharge of Purkinje and cerebellar nuclear neurons during rapidly alternating arm movements in the monkey. Yamamoto K, Kawato M, Kotosaka S, Kitazawa S Encoding of movement dynamics by Purkinje cell simple spike activity during fast arm movements under resistive and assistive force fields. The difference equations and frequency response expressions for these five integrators are presented in Appendix A.

So, again, DSP practitioners typically assume the most accurate integrator is the one whose frequency response most closely matches the frequency response of an ideal integrator. To make such comparisons easier, Figure 3 shows the percent error, versus frequency, between the various integrators and an ideal integrator as computed by:. OK, examine Figure 3 carefully. We might stop at this point and, based on Table 1, assume we have a reliable grasp on the relative accuracy performance of various popular digital integrators.

But to do so would put us on a train bound for Failure City. I support that last statement with the following examples of definite integral results for the seven Test Signal's described in Appendix B. That is, we want to use our five digital integrators to estimate the following definite integral:. When we apply Test Signal 4 to our five integrators their results are listed in the center column of Table 2.

Notice how our measured accuracy ranking of our five digital integrators in the left column of Table 2 is very different than the left column of Table 1. This tells us our initial Table 1 assumptions of integrator accuracy should now be viewed with great suspicion. The integration accuracy percent absolute error results for Test Signal 4, the rightmost column of Table 2, are represented by the center column of symbols in Figure 4.

Our five digital integrators' accuracy error results for the remaining six Test Signals in Appendix B are also shown in Figure 4. In addition, surprisingly, given its minimal frequency-domain error curve in Figure 3 the Al-Alaoui integration method performed rather poorly. My empirical testing, whose results are given in Figure 4, shows that: A digital integrator's accuracy performance is not solely a function of its frequency-domain response. It's performance is also profoundly dependent upon the integrator's input signal.

Thus our naive integrator performance ranking in Table 1, based solely on the frequency-domain error curves in Figure 3, is not valid. When I say "dependent upon the integrator's input signal" I mean dependent, in an unpredictable way, on the input signal's:. This appendix describes the five digital integrators whose accuracy performance is investigated in this blog.

The Rectangular Rule also called the "Backward Rectangular Rule" integration method is defined by the following difference equation:. The Trapezoidal Rule also known as the "Tustin Rule" integration method is defined by the following difference equation:.

Each integrator estimates the definite integral defined by:. These seven test signals were chosen because their true definite integrals are easily found using integral calculus. You probably thought it was too obvious to state that the results for any given signal also depend on the ratio of the sample rate to the bandwidth of the signal.

For example, your signal 4 using Simpsons with samples results in 2. Your last paragraph is absolutely correct and has caused me to add a few words of text to my 'Conclusion' section. In my integrator accuracy modeling I found that the an innocent-looking change to a test input signal may have a very noticeable, and to me unpredictable, change in the integrator's output value. LT1, did you know there is a way to predict the maximum error when using the Trapezoidal Rule integrator to integrate a discrete version of a mathematical function.

That prediction doesn't give us the error in integrating a math function, but rather gives us a value for which the integration error will never exceed. Unfortunately that error "bound" analysis requires that we first use calculus to find the 2nd derivative of the input math function. I wonder if that Trapezoidal Rule error bound analysis can, somehow, be used to predict the max error when integrating real-world signals such as the output of an accelerometer.

I wonder why one would need the integral of position, but as a non-nav guy, I can't be of any help sorry , except perhaps to say I always reach for Simpsons because of the behavior implied above. PS, I was logged in and hit submit reply earlier, but it told me that I had to be logged in to reply and sent my reply to the bit bucket in the sky. Then I logged out and logged back in to generate this. Sorry you had trouble posting a Reply. I've had some of my replies disappear in the past because, in the middle of typing a Reply I clicked on another web page window.

Now I'm much more careful when typing a new Reply. Sorry for the abbreviation. I should have simply said that I have no experience with navigation systems. I don't see any comment on dc gain in any of the integrators. For example the rectangular case has dc gain of 45 dB. Wouldn't a practical integrator need control of this gain.

This is where loop alpha factor comes into action. Hi kaz. Yes, you're right. So their unit impulse responses are non-zero and infinite in length and this gives the integrators a DC gain of infinity. But because these integrators are used to integrate a finite-length sequence of samples, the integrators' single output value will be finite in value.

As such the output for a given input will be affected by how that input is sliced under the transition curve. Would you expect the relative rating of the integrators to be dependent on the 'sampling frequency'? I previously remember you wrote something on the stability of the Goertzel filter which also has a pole on the unit circle. Why is it that there is no trapezoidal differentiator or tick's rule differentiator that is just the inverse of the integration rules?

As we can see from your Fig 2 and 3 we should expect larger differences in performance the more high frequency content is in the input. Also it could be interesting to see if all the different rules actually converge to the true value as sampling frequency goes to infinity. I believe there are applications where such integrators are not used on short input sequences but could run on very long input sequences such as an audio input stream.

And I think that Goertzel filters 'normally' has the pole s adjusted off the unit circle by a tiny value for stability reasons. So I was just wondering if the same would apply to the integrators. But for some reason this seems to not be the case. Yes, the different integration methods do converge toward a true definite integral value as sampling frequency is increased.

Years ago I thought a Goertzel filter was only marginally stable. But I was wrong. A Goertzel filter is guaranteed-stable as I explained at:. T here's no worry about stability with the integrators in my blog as I explained in my above Sept.

I'm experimenting with your notions of Trapezoidal and Simpson differentiators. But I making no useful progress so far. The Goertel filter and the trapezoidal integrator are equivalent same transfer functions for the case when the Goertzel is configured for the DC bin pole at 1 and their impulse responses are constant. For the case that the Goertzel is not computing the DC bin its impulse response oscillates forever with no decay.

No stability issues as long as we make sure that coefficient quantization does not push the pole outside the unit circle and a bounded input results in a bounded output. However in contrast to the trapezoidal integrator the Goertzel needs multiplication with its coefficient to its state variable y[n-1] this is not needed in the trapezoidal integrator. So if the center frequency of the Goertzel is close to DC then numerical stability can be an issue because of round-off errors. From a DSP point of view I think that both the Trapezoidal integrator and the 'Trapezoidal differentiator' should somehow be treated with some caution when it comes to stability.

Because they both have their poles on the unit circle their steady-state responses and transient-responses are merged, the transient not sure if this is the correct name as it continues forever doesn't die out. What I mean by this is that if stability of a filter is thought of as 'the filter not generating tones not present in the input' then both the Trapezoidal integrator and the 'Trapezoidal differentiator' can be thought of as not stable.

This tone added by the 'Trapezoidal differentiator' is perhaps what leaves it very inattractive as the output looks very noisy. Hi Rick. Sorry, yes you are correct. Just wanted to mention that there is a possibility of numerical stability but I don't think there is any point in discussing this. And you are of course correct that I should not have used the term unstable in the 2nd paragraph. Hi niarn. Thanks for the wiki Goertzel web link.

I don't recall ever visiting that interesting web page before. That could be useful for some purposes, but is it "ideal? The proof that such a belief is not valid is the performance of the Al-Alaoui Method integrator. The Al-Alaoui integrator 's freq magnitude response most closely matches an ideal integrator's freq magnitude response, as shown by my blog's Figure 2, but the Al-Alaoui integrator 's accuracy performance is among the worst of all the integrators.

I would just like to ask if you could plot Figure 3 while taking the absolute value of the imaginary part of the functions? The right equation should be 1-z H z. I've made a few plots to show this. The first is comparing rectangular approximation to trapezoid:. Notice that it is about the same amount off as rectangular which explains why their performance is about the same in your tests. Finally, look at trapezoid compared to Simpson's:. Simpson's is significantly smoother. I haven't done the same for Tick's, but I haven't had the time.

What intrigues me is that for function 2, I'd expect the trapezoid and rectangular to give the same result as the first sample is 0 and the last samples is close to 0. I wonder if numerical precision is also playing a role in your results. Your function 1 follows this pattern, which might explain why the results are the best for all methods but Al-Alaoui. Sorry for the long reply. I hope this makes sense. Does the curve you generated for the rectangular rule integrator imply that the y n output sample, for very large values of n, is equal to zero?

Dumb Question: Why is there a logarithm in the equation for the rectangular rule integrator's z-transform equation? And also, sorry, I should have explained the plots a little better. Not a dumb question at all! I did something similar to your Eq. So, the idea is that the mean of the relative errors should be 0 meaning all of these are unbiased estimators.

But I haven't tried to analyze the variance which would probably be more insightful in this matter. My guess is that the variance depends on the number of samples taken, but I won't have time to dig deeper for another few weeks.

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