Computational Intelligence
Zeinab Khatoun Pourtaheri
Abstract
Background and Objectives: According to the random nature of heuristic algorithms, stability analysis of heuristic ensemble classifiers has particular importance. Methods: The novelty of this paper is using a statistical method consists of Plackett-Burman design, and Taguchi for the first time to specify ...
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Background and Objectives: According to the random nature of heuristic algorithms, stability analysis of heuristic ensemble classifiers has particular importance. Methods: The novelty of this paper is using a statistical method consists of Plackett-Burman design, and Taguchi for the first time to specify not only important parameters, but also optimal levels for them. Minitab and Design Expert software programs are utilized to achieve the stability goals of this research. Results: The proposed approach is useful as a preprocessing method before employing heuristic ensemble classifiers; i.e., first discover optimal levels of important parameters and then apply these parameters to heuristic ensemble classifiers to attain the best results. Another significant difference between this research and previous works related to stability analysis is the definition of the response variable; an average of three criteria of the Pareto front is used as response variable.Finally, to clarify the performance of this method, obtained optimal levels are applied to a typical multi-objective heuristic ensemble classifier, and its results are compared with the results of using empirical values; obtained results indicate improvements in the proposed method. Conclusion: This approach can analyze more parameters with less computational costs in comparison with previous works. This capability is one of the advantages of the proposed method.======================================================================================================Copyrights©2020 The author(s). This is an open access article distributed under the terms of the Creative Commons Attribution (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, as long as the original authors and source are cited. No permission is required from the authors or the publishers.======================================================================================================
M. Shakibmehr; M. Lotfizad
Abstract
In this paper, an ultra-low-noise amplifier with frequency band switching capability is designed, simulated and fabricated. The two frequency ranges of this amplifier consist of the 2.4 to 2.5 GHz and 3.1 GHz to 3.15 GHz frequency bands. The designed amplifier has a noise figure of less than 1dB, a minimum ...
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In this paper, an ultra-low-noise amplifier with frequency band switching capability is designed, simulated and fabricated. The two frequency ranges of this amplifier consist of the 2.4 to 2.5 GHz and 3.1 GHz to 3.15 GHz frequency bands. The designed amplifier has a noise figure of less than 1dB, a minimum gain of 23 dB and a VSWR of less than 2 in the whole frequency band. The design process starts with increasing the stability factor in the source through manipulating the inductor placement technique. Then the input and output matching circuits for the first frequency band are designed. This process is completed by utilizing two similar stages placed successively in order to achieve the desired gain level. Since no degradation of the noise figure is observed and acceptable values are also obtained for other parameters, switching the elements in the output matching circuit can be a good idea for avoiding the use of a similar circuit for the second frequency band. The optimum secondary values for the mentioned elements are obtained through the analyses performed using the ADS software. For changing the values of the mentioned elements two MOSFETs are used for adding capacitance and inductance to the matching circuit. In the next step, the designed amplifier is finalized and optimized after adding a suitable bias circuit to it. Moreover, The designed amplifier is fabricated and a good agreement between the measurement, analysis, and simulation results is observed.
