＜正＞ This paper is connected with the problem of selecting archit ectural parameters and learning rate of BP artificial neural network. The self-adapting algorithm is proposed for BP neural network, and the correspo...
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＜正＞ This paper is connected with the problem of selecting archit ectural parameters and learning rate of BP artificial neural network. The self-adapting algorithm is proposed for BP neural network, and the corresponding C language procedure is programmed. It can make the selection of input units, hidden units and learning rate easily in the course of training, reduce external interference and improve the adaptive ability of learning rate and neural network. Our conclusion shows that the self-adapting algorithm of BP artificial neural network superior to the statistical modeling approach and the traditional BP artificial neural network, it can not only exactly imitate training valuation but also make prediction accurately.
Fourier ptychographic microscopy (FPM) can tackle the trade-off between the high resolution and the large field of view. However, the long capturing time limits its application. We propose a self-adapting search algor...
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Fourier ptychographic microscopy (FPM) can tackle the trade-off between the high resolution and the large field of view. However, the long capturing time limits its application. We propose a self-adapting search algorithm for FPM, termed SAS-FPM, which improves the data acquisition efficiency. Here the sparse arrangement is verified via simulations and experiments. Some results demonstrate the effectiveness and efficiency of this method. Compared to FPM with all-capturing mode, SAS-FPM could shorten the acquisition time by more than half.
Remote sensing can be used to monitor cropland phenological characteristics;however, tradeoffs between the spatial and temporal resolutions of cloudless satellite images limit the accuracy of their retrieval. In this ...
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Remote sensing can be used to monitor cropland phenological characteristics;however, tradeoffs between the spatial and temporal resolutions of cloudless satellite images limit the accuracy of their retrieval. In this study, an improved enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) was applied to human-dominated Xiong'an New Area to develop a self-adapting algorithm automating the extraction of main phenological transition points (greenup, maturity, senescence, and dormancy). The analyses of cropland phenological characteristics were performed utilizing the Softmax classification method. By examining three different phases of fusion images, it was found that the improved ESTARFM was more accurate than the original ESTARFM (correlation coefficient > 0.76;relative root mean square error < 0.25;structural similarity index > 0.79). The reconstructed normalized difference vegetation indexes were consistent with that acquired by the Moderate Resolution Imaging Spectroradiometer (average discrepancy: 0.1136, median absolute deviation: 0.0110). The greenup, maturity, senescence, and dormancy points were monitored in 5-day resolution and 50-day length on a 30-m grid scale, and their average day of year (DOY) were 67, 119, 127, and 166 for wheat;173, 224, 232, and 283 for single-season corn;and 189, 227, 232, and 285 for rotation corn, respectively. The corresponding median absolute deviations were 2, 3, 2, and 2 days for wheat;2, 5, 3, and 4 days for single-season corn;and 2, 5, 2, and 2 days for rotation corn, respectively, while all coefficients of variation did not exceed 6%. The proposed self-adapting approach can be used for identifying the planting modes at grid level in rotation agroecosystems and cropland phenological dynamics on a global or regional scale.
In view of the problem that currently the students choose online test questions blindly, the establishment of the item recommendation system is necessary. According to the student's level, an estimating algorithm ...
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ISBN:
(纸本)9781479905614
In view of the problem that currently the students choose online test questions blindly, the establishment of the item recommendation system is necessary. According to the student's level, an estimating algorithm is used to sort items and recommend the question which is in the front of the sort to the student. According to the study of student over a certain period of time and all the answers to the questions of a certain passage that students have given, a multiple attribute decision model based on positive and negative ideal point is established. The model can adjust the level of the student and the difficulty of test question adaptively and ensure that the system will recommend the most suitable test questions.
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