Abstract: In this study we aim at adjusting the singleband and dualband bandpass filter designed in a ED02AH technology. The quality factor and center frequency of the filter will change by varactor diodes. Here, we use a neural network to acquire the proper biasing voltages of varactor diodes in order to obtain specific gain and quality factor

A Alahyari

A Habibzadeh

M Dousti

M Shahrjerdi

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Australian Journal of Basic and Applied Sciences, 5(12): 2162-2167, 2011 
ISSN 1991-8178 
Corresponding Author: A. Alahyari, Sama technical and vocational training college, Islamic Azad University, Broujerd 
Branch, Broujerd, Iran. 
  E-mail: alahyari.amin@gmail.com 
2162 
Designing Tuneable Narrowband Bandpass Filter Utilizing Neural Network 
 
1A. Alahyari, 2M. shahrjerdi, 3A. Habibzadeh, 4M. Dousti 
 
1Sama technical and vocational training college, Islamic Azad University, Broujerd Branch, Broujerd, 
Iran. 
2Department of Electronic , Arak Branch, Islamic Azad University, Arak, Iran. 
3Islamic Azad University- Hashtgerd Branch, Karaj, Iran. 
4Dept. of Electrical Engineering, Science and Research branch, Islamic Azad University, Tehran, Iran. 
 
Abstract: In this study we aim at adjusting the singleband and dualband bandpass filter designed in a 
ED02AH technology. The quality factor and center frequency of the filter will change by varactor 
diodes. Here, we use a neural network to acquire the proper biasing voltages of varactor diodes in order 
to obtain specific gain and quality factor.  
 
Key words: bandpass filter, neural network, ED02AH. 
 
INTRODUCTION 
 
 Tuneable filters have several advantages. They can reduce the complexity of a system avoiding the 
introduction of filter banks. Multi band telecommunication systems, radiometers, and wideband radar systems 
are some of applications of these filters. Previous tuning methods include adjusting the cavity dimensions of the 
resonators or altering the resonant frequency of a ferromagnetic yttrium-iron-garnet element. New approaches 
are based on solid state varactor diodes (Musoll-Anguiano, et al., 2009; Brown, et al., 1999). 
  In this study, we applied neural network to adjust the gain and quality factor of narrowband filter, because 
of its ability to represent RF and microwave component behaviors. Once trained to model the electrical behavior 
of passive and active components/circuits (Fang, et al., 2000; Xu, et al., 2002), They often referred to as neural-
network models, can then be used in high-level simulation and design, providing fast answers to the task they 
have learned (Gupta, 1998; Devabhaktuni, et al., 2001). Moreover, they are efficient alternatives to conventional 
methods such as numerical modeling methods, which could be computationally expensive, or analytical 
methods, which could be difficult to obtain for new devices, or empirical models. This paper organized as 
follows: 
 First, we adjust the two-stage bandpass filter. Then, training of neural network will be discussed. 
Afterwards, tuning the dual band bandpass filter using desired neural network will be addressed.  
 
Tuning Two-Stage Bandpass Filter by Neural Network: 
 Alternatively, a narrowband bandpass filter (BPF) can be constructed by two low-pass filters in a negative 
feedback loop (Alahyari, et al., 2011). The overall structure of a two-stage dualband filter is shown in Fig.1a. It 
is important to notice that, in a single band filter there are only two varactor diodes in the second stage (Va2 and 
Vf2 ) whose biasing voltages can control the center frequency as well as quality factor. Frequency displacement 
is illustrated in Fig.1b. It is prominent that the gain is constant 
 
 
 
Fig. 1a: The circuit of dual band bandpass filter. 
Aust. J. Basic & Appl. Sci., 5(12): 2162-2167, 2011 
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Fig. 1b: Frequency displacement in Two-stage filter. 
 
 In this section we consider the center frequency, quality factor, and desired gain as the neural network 
inputs. The network output specifies the voltages that are appropriate to adjust above parameters. The structure 
of neural network includes three inputs and two outputs. F0 , Q, and A, are perceived as inputs. Network outputs 
are Va and Vf such  that the center frequency and quality factor can be controlled simultaneously using them 
(Fig. 2). 
 
 
 
Fig. 2: Neural Network structure. 
 
Network Training:  
 Several samples (from circuit of the filter) are obtained by ADS software in order to design and train the 
neural network. In fact, These samples are the values of center frequencies and quality factors and gain as a 
function of Va and Vf. That means, we applied Vf and Va (the number of voltages is the number of samples used 
to neural network training) to the circuit and we measured the center frequency and quality factor generated by 
the filter. This sample is used to neural network training via levenberg marquardt algorithm. Three layers of the 
network include; sigmoid function in the first and second layer and hyperbolic tangent in the third layer. After 
training the’ b ‘and ‘w’ factors will be determined using levenberg marquardt algorithm. 
 In order to verify the network performance, some data is applied as a test data. We choose ten numbers of 
Vf and Va as the real values to design the specified filter. The data in the First group in Table. 1 shows them. 
Then, we applied these random data to ADS and new values for center frequency, quality factor, and gain are 
achieved (second group). These values are different from those utilized to network training.  
 Now in order to test a network, the values of center frequencies and quality factors obtained in previous 
step are entered as inputs. The output of this network determines the Vf and Va (third group) and verifies that 
they are close to the first group. The diagrams of Vf and Va as a result of ten samples of center frequency and 
quality factor are illustrated in Fig.3a and Fig.3b.  
 
 
 
Fig. 3a: Compassion diagram of real Vf and network output Vf. 
Aust. J. Basic & Appl. Sci., 5(12): 2162-2167, 2011 
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Fig. 3b: Compassion diagram of real Va and network output Va. 
 
Table 1: Real data and obtained data from neural network. 
First group Second group Third group Fourth group 
Vf Va f0 Q A Vf Va f0 Q A 
-1.45 -0.59 2.09 70 38.95 -1.7844 -0.6129 2.11 75 44.74 
-2.15 -0.81 2.145 72 41.73 -2.0032 -0.7302 2.13 72 43.67 
-3.05 -1.15 2.206 75 40.30 -2.9437 -1.1180 2.22 78 41.6 
-4.45 -1.64 2.280 72 41.21 -4.4487 -1.6409 2.28 70 41.29 
-4.85 -1.91 2.300 70 39.60 -4.5587 -1.8248 2.29 68 38.75 
-5.65 -2.12 2.325 70 40.30 -5.3071 -2.0343 2.32 72 41.12 
 As a case of comparison, we apply one real voltage ( first group) and obtained voltage from neural network 
(third group) to desired filter. Simulation results depict that two filters are similar to each other (Fig. 4) 
 
Line color Vf Va f0 Q A 
-3.05 -1.15 2.206 75 40.30 
 -2.9437 -1.1180 2.22 78 41.6 
 
 
 
Fig. 4: Compassion result of a real filter and its counterpart achieved by neural network. 
 
Tuning two-stage dual band bandpass filter by neural network: 
 The structure of a dualband bandpass filter is introduced above (see Fig.1). It includes two varactor diodes 
in the first and second stage respectively. As mentioned before, biasing voltages variation is applied to adjust f0 
and Q. Frequency displacement is depicted in Fig. 5.  
 
 
 
Fig. 5: Frequency displacement in dual band bandpass filte 
Aust. J. Basic & Appl. Sci., 5(12): 2162-2167, 2011 
2165 
 Since each stage works separately and biasing voltage variation in one stage does not affect on the second, 
the neural network for each stage is designed independently. Similar structure is employed as discussed in two-
stage filter. In this case, we train the neural network using achieved samples from ADS and finally we will test 
the network with seven new data. Fig.6a and Fig.6b show the behavior of the first stage dual band filter. Values 
of Va and Vf from network output; seven samples of center frequency, quality factor and gain used as testing 
input; are compared with real Va and Vf. 
 
 
 
Fig. 6 a: Diagram of Vf1 of the first stage dual band filter. 
 
 
Fig. 6 b: Diagram of Va1 of the first stage dual band filter. 
 
Table.2: Real data and obtained data from neural network 
First group  Second group  Third group Fourth group 
Vf1 Va1 f1 Q1 A1 Vf1 Va1 f1 Q1 A1 
0.23 0.24 1.780 95 50.19 0.2258 0.3757 1.755 60 28.95 
0.48 0.26 1.7 85 40.00 0.4687 0.2563 1.69 80 42.00 
0.25 0.23 1.775 80 44.24 0.2585 0.1391 1.795 65 33.45 
0.10 0.20 1.820 90 63.41 0.1073 0.2003 1.820 90 63.56 
0.35 0.24 1.745 85 42.06 0.3358 0.2529 1.750 90 46.38 
0.52 0.27 1.665 80 41.12 0.5308 0.2885 1.675 80 39.96 
0.40 0.26 1.720 90 45.31 0.3686 0.2688 1.735 110 54.40 
The same test method is exerted on the second stage of a dualband filter. Fig.7a and 7b illustrate the comparison results. 
 
 
 
Fig. 7a: Diagram of Vf2 of the second stage dual band filter 
Aust. J. Basic & Appl. Sci., 5(12): 2162-2167, 2011 
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Fig. 7b: Diagram of Va2 of the second stage dual band filter 
 
Table 3: Real value and neural network output value 
First group  Second group  Third group Fourth group 
Vf2 Va2 f2 Q2 A2 Vf2 Va2 f2 Q2 A2 
-4.35 -1.23 2.21 60 32.02 -4.5737 -1.2786 2.215 65 32.30 
-1.95 -0.52 2.095 90 49.80 -2.3407 -0.5803 2.075 110 47.50 
-3.25 1.00 2.145 110 54.23 -3.0726 -0.8318 2.145 85 42.60 
-2.10 -0.60 2.09 85 43.33 -2.0864 -0.5517 2.085 85 45.95 
-5.30 -1.40 2.23 85 43.90 -4.7897 -1.3455 2.225 65 33.20 
-4.90 -1.25 2.22 75 39.22 -5.1536 -1.2578 2.225 75 38.88 
-2.80 -0.70 2.125 85 44.01 -2.7244 -0.7112 2.120 85 42.34 
 In the following of this study, two samples of both Table 2 and Table 3 are applied to the filter. One real voltage value (first group) and 
one value obtained from neural network (third group). As Fig.8a and Fig.8b imply, the diagrams are adjacent to each other.  
 
Vf2 Va2 Vf1 Va1  
-1.95 -0.52 0.48 0.26 Real value 
 
-2.34 -0.58 0.4687 0.2563 Network output 
   
 
 
 
Fig. 8a: Comparison of real filter and obtained filter from neural network (data of Table 2). 
 
 
 
 
Fig. 8b: Comparison of real filter and obtained filter from neural network(data of Table 3). 
Vf2 Va2 Vf1 Va1  
  -4.90  -1.25  0.52  0.27 Real value 
 
-5.1536 -1.2578 0.5308   0.2885 Network output 
   
Aust. J. Basic & Appl. Sci., 5(12): 2162-2167, 2011 
2167 
Conclusion: 
 As depicted in this study, using biasing voltages of varactor diodes we could control the center frequency as 
well as quality factor. The filter behaviour obtained from neural network output is very similar to a real filter. In 
further works, we can do similar estimation utilizing fuzzy systems and comparing the results with neural 
network. We used ED02AH technology in this paper. We aim to achieve filters with wider bandwidth while 
applying other technologies such as BICMOS or RFCMOS in the same structure.  
 
REFERENCES 
  
Alahyari, A., A. Habibzadeh, M. Dousti, 2011. “Tunable Active Dual-Band Bandpass Filter Design Using 
MMIC Technology,” International Journal of Engineering & Technology IJET-IJENS, 11(01). 
Brown, A.R., Student Member, IEEE, Gabriel M. Rebeiz, Fellow, 1999. “A Varactor Tuned RF Filter,” 
 Devabhaktuni, V.K., M. Yagoub, Y. Fang, J. Xu and Q.J. Zhang, 2001. “Neural networks for microwave 
modeling: Model development issues and nonlinear modeling techniques,” Int. J. RF Microwave Computer- 
Aided Eng., 11: 4-21. 
Fang, Y., M. Yagoub, F. Wang and Q.J. Zhang, 2002. “A new macro modeling approach for nonlinear 
microwave circuits based on recurrent neural networks,” IEEE Trans. Microwave Theory Tech., 48: 2335-2344.  
 Gupta, K.C., 1998. “Emerging trends in milli meter-wave CAD. ” IEEE Trans. Microwave Theory Tech., 
46: 747-755. 
 IEEE Trans. On MTT, October 29, 1999. DOI:10.1109/22.848501. 
Musoll-Anguiano, C., I. Llamas-Garro, Z. Brito-Brito, L. Pradelll, Al. Corona-Chavez, 2009. 
“Characterizing a Tune All Bandstop Filter,” IEEE MTT-S International Microwave Workshop Series on Signal 
Integrity and High-Speed Interconnects (IMWS2009-R9), DOI: 10.1109/IMWS.2009.4814908. 
Xu, J., M. Yagoub, R. Ding, and Q.J. Zhang, “Neural-based dynamic modeling of nonlinear microwave 
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Designing Tuneable Narrowband Bandpass Filter Utilizing Neural Network

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Designing Tuneable Narrowband Bandpass Filter Utilizing Neural Network

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