Design of Multi-Layer Neural Networks for Butterworth Filter Optimization (original) (raw)
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International Journal of Computer Applications, 2014
For the design of Band pass FIR filters complex calculations are involved. Mathematically, by substituting the value of pass-band ripple, stop band attenuation, pass-band frequency F1, pass-band frequency F2, sampling frequency in any of the methods from window method, frequency sampling method or optimal method we can get the values of filter coefficients h(n). Here, window method is used in which Kaiser window method has been chosen preferably because of the presence of ripple factor (β).Here, I have design Band pass FIR filter using artificial neural network which gives optimum result i.e. the difference between the actual and desired output is minimum.
IJERT-Design of Band-Pass Filter using Artificial Neural Network
International Journal of Engineering Research and Technology (IJERT), 2014
https://www.ijert.org/design-of-band-pass-filter-using-artificial-neural-network https://www.ijert.org/research/design-of-band-pass-filter-using-artificial-neural-network-IJERTV3IS20290.pdf For the design of Band pass FIR filters complex calculations are required. Mathematically, by replacing the values of pass-band ripple, stop band attenuation, pass-band frequency F1, pass-band frequency F2, sampling frequency in any of the methods from window method, frequency tasting method or optimal method we can get the values of filter coefficients h(n). Here, window method is used in which Kaiser window method has been chosen preferably because of the presence of ripple factor (β).Here, I have design Band pass FIR filter using artificial neural network which gives optimum result i.e. the difference between the actual and desired output is minimum.
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International Journal of Computer Applications, 2014
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Digital Signal Processing is an advanced technology that will determine the direction of science and technology in the next centuries. One of the main direction of digital signals processing is digital filters, which in the most cases have advantages over analog filters. Currently there are various methods of filter analysis and design. In this work, for synthesis of all types of recursive filters (low pass, high pass, bandwidth, band stop) is used a neural network. The main objective of filter synthesis is to find the filter coefficients. These filter coefficients define the filter transfer function. Using an iterative procedure of the neural network such as Backpropagation algorithm, on base of Visual C++ software was developed the program, which designs recursive filters with required characteristics. This is particularly important for the designing of the new correcting filters characteristics, the purpose of which is to reduce the unwanted noises in the measurement signal.
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In this work, we propose a novel technique based on neural networks, for the design of microwave filters in shielded printed technology. The technique uses radial basis function neural networks to represent the non linear relations between the quality factors and coupling coefficients, with the geometrical dimensions of the resonators. The radial basis function neural networks are employed for the first time in the design task of shielded printed filters, and permit a fast and precise operation with only a limited set of training data. Thanks to a new cascade configuration, a set of two neural networks provide the dimensions of the complete filter in a fast and accurate way. To improve the calculation of the geometrical dimensions, the neural networks can take as inputs both electrical parameters and physical dimensions computed by other neural networks. The neural network technique is combined with gradient based optimization methods to further improve the response of the filters. Results are presented to demonstrate the usefulness of the proposed technique for the design of practical microwave printed coupled line and hairpin filters. © 2008 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2009.
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Today, there exist many examples of artificial neural network (ANN) technology implementations. By far the most successful of these have been with multilayer feedforward networks, primarily utilizing the backpropagation (BPN) paradigm. These networks are universal classifiers and as such are able to address various engineering problems. However, the designing and building of these networks is not well defined. In fact, there may not exist a practical step-by-step method of design which can be broadly applied since theoretically there are an infinite number of configurations which would have to be tested to identify the optimal design. Practically, if certain network parameters are bounded over a reasonable range it is possible to design an optimal network within these guidelines. In this paper, a BPN network is designed by applying this method. The results suggest the method is efficient, reliable and probably yields the absolute optimal network. The nonlinear systems modeling and simulation problem, power flow analysis, is undertaken with the BPN network being compared with the classical Newton-Raphson method.