Essay On Digital Filter

The programmed algorithm is shown in Figure 6.The program was developed using LabVIEW System design software. The entire experimental set-up is shown in Figure 7.

The following steps are used to design the back propagation neural network algorithm for the proposed research work. The first step is to set the input, output data sets. The second step is to set the number of hidden layer and output activation functions. The third step is to set the training functions and training parameters, finally run the network.

There are many different types of filters that can be used for decimating, including: cascaded comb-integrator (CIC); direct form polyphase finite impulse response (FIR); half-band FIR; symmetric polyphase FIR filters; transposed direct form II infinite impulse response (IIR) and quasi-linear IIR filters amongst others. As well as using these different configurations, hybrid types can be used for more efficient implementations designed either for better magnitude response, lower power consumptions or

Digital Filter. An on-chip digital filter is used to minimize the signals and noise that are outside the band of interest.

For standard backpropagation algorithm, an approximate steepest descent rule will be used. The performance index is approximated by equation

Simulate or run the program in PSPICE Software. 4). Note the maximum output voltage and the cut off frequency from the graph displayed by PROBE graphics processor. 5). Compare the simulated value of the maximum voltage gain and the cut off frequency with the theoretical maximum voltage gain and the theoretical cut off frequency.

The training is divided into two phases: learning phase and testing phase. In the learning phase, an iterative which updated the synoptic weights is formed upon the error BP (Back Propagation) algorithm. In the testing phase the number of input and output parameters as well as the cases number influenced the neural network,whereas the trained results is then compared to the target to make a decision about the continuing of the iteration or the obtained results is concluded. The common ANN structure for the three architectures is (3X3), which means three neurons in the input layer and three neurons in the hidden layer. The training of each ANN architecture designs are shown in the following: fig.3, fig.4 and fig.5,

The comparison of above three algorithms for 8, 16 and 32 bit operands with corresponding voltage and frequency are tabulated in table I

TITLE: To overcome the coefficient sensitivity for the poles of pipelined coefficient using angle based constrained in IIR filter.

A filter is a designed circuit which act to pass signals with desired frequencies and reject or attenuate others. If the filter consists of passive element that are resistor (R), inductor (L) and capacitor (C), it is called as passive filter. Meanwhile, active filter consists of active elements such as transistors and op amps alongside the resistor (R), inductor

A Low Pass filter lets through the frequencies below the cutoff frequency and eliminates the ones above it. An Example of a low pass filter can be found on track 9 of my logic session which is with my assessment.

A linear formula idea will be used and the decision variables will be labeled as follow:

The first was a simple bandpass filter, and after trial and error I chose the following parameters; 30 to 40 Hz on the low and 70 to 90 Hz on the high end. Following the bandpass I removed a spike in the signal at 60 Hz with a Notch filter ranging from 55 to 58 Hz and 62 to 65 Hz.

Following that, the expected values for decision nodes 6 and 7 should also be calculated. The following results were obtained:

Using TI InterActive!™ to graph that function, overlaid with the original data, and restricting the graph of the function to the same domain and range as the domain and range on the scatterplot, the graph is as follows: