Analog active filter design using a multi objective genetic algorithm

Mostafa, Sheikh Shanawaz; Horta, Nuno; Ravelo-García, Antonio G.; Morgado‐Dias, Fernando

doi:10.1016/j.aeue.2018.06.001

Cited by 17 publications

(18 citation statements)

References 36 publications

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“…CNN's hyperparameters were optimized using a multi objective genetic algorithm named NSGA-II [49] [62]. The multi objective technique was used to have an equally better performance in all objectives, contrary to what it is present in the single objective optimization method [50]. A Multi objective optimization technique optimizes a vector ( ) of objective functions (in this work Acc, Sen and Spc) and the optimization consists of finding (in this case, the hyperparameters) which maximizes ( ) represented as…”

Section: Optimization Of Cnn Hyperparameters Using Multi-objectivementioning

confidence: 99%

“…In the first step, the algorithm generates a random population to ensures the diversity of the population and rank them according to the multi objective optimization (Acc, Sen and Spc) [50]. From Fig.…”

Section: A Hyperparameter Optimizationmentioning

confidence: 99%

“…The designed model addresses this issue by simultaneously considering the accuracy (Acc), sensitivity (Sen) and specificity (Spc) in a multi-objective problem. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) [49] was selected for this work due to the large success it has in other areas, such as filter design [50], water distribution system [51]. Therefore, the primary objectives of this work are:…”

Section: Introductionmentioning

confidence: 99%

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Multi-Objective Hyperparameter Optimization of Convolutional Neural Network for Obstructive Sleep Apnea Detection

et al. 2020

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Obstructive sleep apnea (OSA) is a common sleep disorder characterized by interrupted breathing during sleep. Because of the cost, complexity, and accessibility issue related to polysomnography, the gold standard test for apnea detection, automation of the diagnostic test based on a simpler method is desired. Several signals can be used for apnea detection, such as airflow and electrocardiogram. However, the reduction of airflow normally leads to a decrease in the blood oxygen saturation level (SpO2). This signal is usually measured by a pulse oximeter, a sensor that is cheap, portable, and easy to assemble. Therefore, the SpO2 was chosen as the reference signal. Feature based classifiers with shallow neural networks have been developed to provide apnea detection using SpO2. However, two main issues arise, the need for feature creation and the selection of the more relevant features. Deep neural networks can solve these issues by employing featureless methods. Among multiple deep classifiers that have been developed, convolution neural networks (CNN) are gaining popularity. However, the selection of the CNN structure and hyperparameters are typically done by experts, where prior knowledge is essential. With these problems in mind, an algorithm for automatic structure selection and hyper parameterization of a one dimension CNN was developed to detect OSA events using only the SpO2 signal. Three different input sizes and databases were tested, and the best model achieved an average accuracy, sensitivity, and specificity of 94%, 92%, and 96%, respectively.

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Section: Optimization Of Cnn Hyperparameters Using Multi-objectivementioning

confidence: 99%

Section: A Hyperparameter Optimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multi-Objective Hyperparameter Optimization of Convolutional Neural Network for Obstructive Sleep Apnea Detection

et al. 2020

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“…This study is considered closer to our study and its results have been compared to our results. In another study by Mostafa et al (2018), eight components are applied as variables for improvement, as these components are compatible with the E96, E24, and E12 series by the genetic algorithm II (NSGA-II) with two analog filters which are a second order-filter and a fourth-order Butterworth with the operational amplifiers for testing Pertanika J. Sci. & Technol.…”

Section: Introductionmentioning

confidence: 99%

Applying Genetic Algorithm to Optimization Second-Order Bandpass MGMFB Filter

Shukur¹,

Abttan²

2020

JST

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In this paper, we have applied the genetic algorithm to the selection of the true values for RC (resistors/capacitors) as an essential role in the development of analogue active filters. The classic method of incorporating passive elements is a complex situation and can attend to errors. In order to reduce the frequency of errors and the human effort, evolutionary optimization methods are employed to select the RC values. In this study, Genetic algorithm (GA) is proposed to optimize the second-order active filter. It must find the values of the passive elements RC to get a filter configuration that reduces the sensitivities to variations as well as reduces design errors less than a defined height value, concerning certain specifications. The optimization problem which is one of the problems that must be solved by GA is a multi-objective optimization problem (MOOP). GA was carried out taking into account two possible situations about the values that resistors and capacitors could adopt. The obtained experimental results show that GA can be used to obtain filter configurations that meet the specified standard.

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“…The use of optimization algorithms [26,27], and, specifically, of genetic algorithm (GA) is widely spread for electromagnetic problems [28][29][30][31]. The GA represents a good candidate for successfully optimize the decap placement of the PDN in PCBs, due to the randomness of its core algorithm that is able to effectively take into account the full range of possible combination of decap type and locations.…”

Section: Introductionmentioning

confidence: 99%

Efficient Iterative Process Based on an Improved Genetic Algorithm for Decoupling Capacitor Placement at Board Level

et al. 2019

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To reduce the noise created by a power delivery network, the number, the value of decoupling capacitors and their arrangement on the board are critical to reaching this goal. This work deals with specific improvements, implemented on a genetic algorithm, which used for the optimization of the decoupling capacitors in order to obtain the frequency spectrum of the input impedance in different positions on the network, below previously defined values. Measurements are performed on a specifically manufactured board in order to validate the effectiveness of the proposed algorithm and the optimization results obtained for a specific example board.

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Analog active filter design using a multi objective genetic algorithm

Cited by 17 publications

References 36 publications

Multi-Objective Hyperparameter Optimization of Convolutional Neural Network for Obstructive Sleep Apnea Detection

Multi-Objective Hyperparameter Optimization of Convolutional Neural Network for Obstructive Sleep Apnea Detection

Applying Genetic Algorithm to Optimization Second-Order Bandpass MGMFB Filter

Efficient Iterative Process Based on an Improved Genetic Algorithm for Decoupling Capacitor Placement at Board Level

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