Because of the control problem complexity, engine calibration becomes a multi-object nonconvex optimization problem (MONCOP). To solve this problem and shorten the calibration time, this study proposes an innovative o...
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Because of the control problem complexity, engine calibration becomes a multi-object nonconvex optimization problem (MONCOP). To solve this problem and shorten the calibration time, this study proposes an innovative on-line engine calibration algorithm which combines momentum gradient descent algorithm with search space division (MGD-SSD). Firstly, taking a two-stroke kerosene engine as the research object, the engine response model is built by support vector machine (SVM), and based on the analysis of the spark timing and air-to-fuel ratio calibration problem, MAPs of objective function value under three typical engine operation conditions are established. Then the design of MGD-SSD is introduced in detail. The search space division is used to find the appropriate initial point quickly and the momentum gradient descent algorithm is used to quickly find the global best point through the appropriate initial point. A comparative study on three methods including genetic algorithm (GA), genetic and gradientdescent hybrid algorithm (GGD) and MGD-SSD is carried out through benchmark function test and virtual calibration test. All tests show that the MGD-SSD is able to find the global optimal solution with higher accuracy and fewer iterations. Finally, the bench test of MGD is carried out, and the validity of the algorithm is verified.
The emission parameters of cognitive radars can adaptively change according to the environment, which poses a challenge to radar electronic countermeasures (ECM). To counter cognitive radars, it is essential to identi...
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The emission parameters of cognitive radars can adaptively change according to the environment, which poses a challenge to radar electronic countermeasures (ECM). To counter cognitive radars, it is essential to identify the cognitive characteristics. In this paper, a method is proposed to recognize cognitive radars with power allocation function. The signal-to-interference-plus-noise ratio (SINR) distribution of cognitive radars is derived through feature functions, and hypothesis test is used to identify whether the target radar has cognitive function by designing a Kolmogorov-Smirnov (K-S) detector to recognize adaptive optimization power allocation. Subsequently, a momentum gradient descent algorithm is used to optimize the signal of the jamming machine to reduce type II error probability of radar recognition. K-S detector is simulated and compared with Afriat detector, SVM and MLP detector. Results demonstrate that the K-S detector outperforms both the Afriat and MLP detectors in identifying cognitive radars with dynamic power allocation functionality. At the same detection probability, the K-S detector achieves a 2 dB improvement over the MLP detector and a 4 dB improvement over the Afriat detector.
Data are a strategic resource for industrial production, and an efficient data-mining process will increase productivity. However, there exist many missing values in data collected in real life due to various problems...
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Data are a strategic resource for industrial production, and an efficient data-mining process will increase productivity. However, there exist many missing values in data collected in real life due to various problems. Because the missing data may reduce productivity, missing value imputation is an important research topic in data mining. At present, most studies mainly focus on imputation methods for continuous missing data, while a few concentrate on discrete missing data. In this paper, a discrete missing value imputation method based on a multilayer perceptron (MLP) is proposed, which employs a momentum gradient descent algorithm, and some prefilling strategies are utilized to improve the convergence speed of the MLP. To verify the effectiveness of the method, experiments are conducted to compare the classification accuracy with eight common imputation methods, such as the mode, random, hot-deck, KNN, autoencoder, and MLP, under different missing mechanisms and missing proportions. Experimental results verify that the improved MLP model (IMLP) can effectively impute discrete missing values in most situations under three missing patterns.
Data have become an important factor of production in various fields. While there are many missing values due to some irresistible reasons, which may lead to incorrect results and conclusions. A set of imputation meth...
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ISBN:
(纸本)9781665426053
Data have become an important factor of production in various fields. While there are many missing values due to some irresistible reasons, which may lead to incorrect results and conclusions. A set of imputation methods have been proposed for filling continuous data, while only a few works focus on discrete missing data. In this paper, we propose an improved multi-layer perceptron (IMLP) imputation method based on the momentumgradientdescent (MGD) algorithm to impute the discrete missing data via the one-hot encoding technique. To verify the performance of this method, comparisons have been made of mode, K-nearest neighbor, and auto-encoder imputation methods on different missing patterns and missing rates. The simulation result shows that the IMLP has a good imputation performance on discrete data with a high missing rate.
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