文献类型：期刊文献

英文题名：Neural Network Optimization Method and Its Application in Information Processing

作者：Wang, Pin[1];Wang, Peng[2];Fan, En[3]

机构：[1]Shenzhen Polytech, Sch Mech & Elect Engn, Shenzhen 518055, Guangdong, Peoples R China;[2]Chinese Acad Sci, Garden Ctr, South China Bot Garden, Guangzhou 510650, Guangdong, Peoples R China;[3]Shaoxing Univ, Dept Comp Sci & Engn, Shaoxing 312000, Zhejiang, Peoples R China

年份：2021

卷号：2021

外文期刊名：MATHEMATICAL PROBLEMS IN ENGINEERING

收录：SCI-EXPANDED(收录号：WOS:000621819600007)、、EI(收录号：20210909993118)、Scopus(收录号：2-s2.0-85101574312)、WOS

基金：This work was supported by the National Natural Science Foundation of China under Grant 61703280.

语种：英文

外文关键词：Adaptive control systems - Array processing - Computation theory - Decision making - Direction of arrival - Drops - Gaussian noise (electronic) - Mapping - Network layers - Nonlinear dynamical systems - Self organizing maps - Signal to noise ratio - Topology - Vector spaces

外文摘要：Neural network theory is the basis of massive information parallel processing and large-scale parallel computing. Neural network is not only a highly nonlinear dynamic system but also an adaptive organization system, which can be used to describe the intelligent behavior of cognition, decision-making, and control. The purpose of this paper is to explore the optimization method of neural network and its application in information processing. This paper uses the characteristic of SOM feature map neural network to preserve the topological order to estimate the direction of arrival of the array signal. For the estimation of the direction of arrival of single-source signals in array signal processing, this paper establishes a uniform linear array and arbitrary array models based on the distance difference vector to detect DOA. The relationship between the DDOA vector and the direction of arrival angle is regarded as a mapping from the DDOA space to the AOA space. For this mapping, through derivation and analysis, it is found that there is a similar topological distribution between the two variables of the sampled signal. In this paper, the network is trained by uniformly distributed simulated source signals, and then the trained network is used to perform AOA estimation effect tests on simulated noiseless signals, simulated Gaussian noise signals, and measured signals of sound sources in the lake. Neural network and multisignal classification algorithms are compared. This paper proposes a DOA estimation method using two-layer SOM neural network and theoretically verifies the reliability of the method. Experimental research shows that when the signal-to-noise ratio drops from 20 dB to 1 dB in the experiment with Gaussian noise, the absolute error of the AOA prediction is small and the fluctuation is not large, indicating that the prediction effect of the SOM network optimization method established in this paper does not vary. The signal-to-noise ratio drops and decreases, and it has a strong ability to adapt to noise.