Temperature drift modeling and compensation of capacitive accelerometer based on AGA-BP neural network

Han, Zijie; Hong, Li; Meng, Juan; Li, Yanan; Gao, Qiang

doi:10.1016/j.measurement.2020.108019

Cited by 35 publications

(15 citation statements)

References 16 publications

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“…Compared with Case 1, it is obvious that the AGA-BPNN algorithm shows better generalization ability and calculation accuracy than those of the GA-BPNN algorithm. Regarding the performance of the GA-BPNN algorithm and the AGA-BPNN algorithm, Han et al applied the algorithms to the test of compensating temperature drift and found that the AGA-BPNN algorithm shows a better performance than the BPNN algorithm, thereby effectively avoiding the local optimal value [37]. is provides support for the research results obtained in this study.…”

Section: Results For Simulated Analysis Of Casesupporting

confidence: 76%

Optimization of Backpropagation Neural Network under the Adaptive Genetic Algorithm

2021

Complexity

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This study is to explore the optimization of the adaptive genetic algorithm (AGA) in the backpropagation (BP) neural network (BPNN), so as to expand the application of the BPNN model in nonlinear issues. Traffic flow prediction is undertaken as a research case to analyse the performance of the optimized BPNN. Firstly, the advantages and disadvantages of the BPNN and genetic algorithm (GA) are analyzed based on their working principles, and the AGA is improved and optimized. Secondly, the optimized AGA is applied to optimize the standard BPNN, and the optimized algorithm is named as OAGA-BPNN. Finally, three different cases are proposed based on the actual scenario of traffic flow prediction to analyse the optimized algorithm on the matrix laboratory (MATLAB) platform by simulation. The results show that the average error distribution of the GA-BPNN algorithm is about 1% with small fluctuation range, better calculation accuracy, and generalization performance in contrast to the BPNN. The average output error of the AGA-BPNN fluctuates around 0 and remains in a relatively stable range as a whole in contrast to that of GA-BPNN; the maximum fitness level keeps increasing during the evolution process but approaches the average value in later process, so the population diversity is hard to be guaranteed. The output error of the OAGA-BPNN fluctuates little compared with that of AGA-BPNN, and its maximum fitness continues to increase in the evolution process with guaranteed population diversity. In short, the OAGA-BPNN algorithm can achieve the best performance in terms of calculation accuracy, generalization performance, and population evolution.

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Section: Results For Simulated Analysis Of Casesupporting

confidence: 76%

Optimization of Backpropagation Neural Network under the Adaptive Genetic Algorithm

2021

Complexity

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“…Elakkiya and Selvakumar [ 36 ] used enhanced step size firefly algorithm to generate optimal weights of feedforward neural network for spam detection. In [ 37 ], adaptive GA has been proposed for weight optimization of BPNN for capacitive accelerometers. The optimized BPNN is used in the capacitive accelerometer.…”

Section: Related Workmentioning

confidence: 99%

“…Several learning algorithms exist in the literature with the aim of finding an optimal MLP. These learning algorithms can be broadly classified into three categories, namely, conventional methods [ 8 – 12 ], metaheuristic-based methods [ 13 – 37 ], and hybrid methods [ 20 , 38 – 44 ].…”

Section: Introductionmentioning

confidence: 99%

GGA‐MLP: A Greedy Genetic Algorithm to Optimize Weights and Biases in Multilayer Perceptron

Bansal

Lamba

Jain

et al. 2022

Contrast Media & Molecular Imaging

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The task of designing an Artificial Neural Network (ANN) can be thought of as an optimization problem that involves many parameters whose optimal value needs to be computed in order to improve the classification accuracy of an ANN. Two of the major parameters that need to be determined during the design of an ANN are weights and biases. Various gradient-based optimization algorithms have been proposed by researchers in the past to generate an optimal set of weights and biases. However, due to the tendency of gradient-based algorithms to get trapped in local minima, researchers have started exploring metaheuristic algorithms as an alternative to the conventional techniques. In this paper, we propose the GGA-MLP (Greedy Genetic Algorithm-Multilayer Perceptron) approach, a learning algorithm, to generate an optimal set of weights and biases in multilayer perceptron (MLP) using a greedy genetic algorithm. The proposed approach increases the performance of the traditional genetic algorithm (GA) by using a greedy approach to generate the initial population as well as to perform crossover and mutation. To evaluate the performance of GGA-MLP in classifying nonlinear input patterns, we perform experiments on datasets of varying complexities taken from the University of California, Irvine (UCI) repository. The experimental results of GGA-MLP are compared with the existing state-of-the-art techniques in terms of classification accuracy. The results show that the performance of GGA-MLP is better than or comparable to the existing state-of-the-art techniques.

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“…Furthermore, in order to improve the nonlinear adaptability of the algorithm, a neural network is adopted to identify the bias and scale factors [ 15 ]. Research has shown that it can better fit the model to put the input acceleration and temperature as input variables of the neural network [ 16 ]. Compared with the hardware compensation, the software compensation is more suitable for fast and stable starting of the accelerometer for the advantage of avoiding the extra volume and weight and being easier to realize.…”

Section: Introductionmentioning

confidence: 99%

Cold Starting Temperature Drift Modeling and Compensation of Micro-Accelerometer Based on High-Order Fourier Transform

et al. 2022

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The traditional temperature modeling method is based on the full heating of the accelerometer to achieve thermal balance, which is not suitable for the cold start-up phase of the micro-accelerometer. For decreasing the complex temperature drift of the cold start-up phase, a new temperature compensation method based on a high-order Fourier transform combined model is proposed. The system structure and repeatability test of the micro digital quartz flexible accelerometer are provided at first. Additionally, we analyzed where the complex temperature drift of the cold start-up phase comes from based on the system structure and repeatability test. Secondly, a high-order temperature compensation model combined with K-means clustering and the symbiotic organisms search (SOS) algorithm is established with repeatability test data as training data. To verify the proposed temperature compensation model, a test platform was built to transmit the measured values before and after compensation with the proposed Fourier-related model and the other time-related model, which is also a model aiming at temperature compensation in the cold start-up phase. The experimental results indicate that the proposed method achieves better compensation accuracy compared with the traditional temperature compensation methods and the time-related compensation model. Furthermore, the compensation for the cold start-up phase has no effect on the original accuracy over the whole temperature range. The stability of the accelerometer can be significantly improved to about 30 μg in the start-up phase of different temperatures after compensation.

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Temperature drift modeling and compensation of capacitive accelerometer based on AGA-BP neural network

Cited by 35 publications

References 16 publications

Optimization of Backpropagation Neural Network under the Adaptive Genetic Algorithm

Optimization of Backpropagation Neural Network under the Adaptive Genetic Algorithm

GGA‐MLP: A Greedy Genetic Algorithm to Optimize Weights and Biases in Multilayer Perceptron

Cold Starting Temperature Drift Modeling and Compensation of Micro-Accelerometer Based on High-Order Fourier Transform

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