摘 要
关键词：神经网络；信号处理；应用研究
Neural networks, as an important branch of artificial intelligence, have become a hot topic in today's technological research due to their unique network structure optimization, powerful generalization ability, continuous optimization, and adaptability. This article first elaborates on the basic principles of neural networks, including their definition, basic composition, and significant characteristics. Neural networks are composed of a large number of interconnected neurons, which simulate the working mode of the human brain nervous system and achieve parallel processing and self-learning functions of information. Its characteristics are reflected in the flexibility of the network structure, strong representation learning ability, and good adaptability, which enable neural networks to handle complex nonlinear problems. In the field of signal processing, the application of neural networks is becoming increasingly widespread. This article explores the applications of neural networks in signal recognition and classification, noise suppression and filtering, signal prediction, and data compression and decompression. By analyzing examples, the excellent performance and practicality of neural networks in signal processing were demonstrated. However, neural networks also face a series of challenges in signal processing applications, such as limited high-dimensional data processing capabilities, limitations in modeling temporal relationships, difficulty in processing sparse data, and high computational complexity and resource consumption. These challenges limit the application effectiveness of neural networks in certain specific scenarios. To overcome these challenges, this article proposes corresponding response strategies. By processing data in batches, the difficulty of processing high-dimensional data can be reduced; The use of recurrent neural networks can better capture temporal relationships; The use of data interpolation or filling methods can effectively handle sparse data; Optimizing network structure and parameters can help reduce computational complexity and resource consumption. These strategies provide useful references and guidance for the application of neural networks in the field of signal processing.
Keywords: Neural networks; Signal processing; application research
目录
Abstract II
第1章 绪论 1
1.1 研究背景及意义 1
1.2 研究目的及内容 1
1.3 国内外研究现状 2
第2章 神经网络基本原理 3
2.1 神经网络的定义 3
2.2 神经网络的基本构成 3
2.3 神经网络的特点 4
2.3.1 网络结构优化 4
2.3.2 强大的泛化能力 4
2.3.3 持续优化和自适应性 4
第3章 神经网络在信号处理中的应用 6
3.1 信号识别和分类 6
3.2 噪声抑制和滤波 6
3.3 信号预测 6
3.4 数据压缩与解压 7
第4章 神经网络在信号处理应用中面临的挑战 8
4.1 高维数据处理能力有限 8
4.2 时序关系建模的局限性 8
4.3 稀疏数据处理难度大 8
4.4 计算复杂度和资源消耗高 9
第5章 神经网络在信号处理应用中的应对策略 10
5.1 数据分批处理 10
5.2 使用循环神经网络 10
5.3 采用数据插值或填充方法 11
5.4 优化网络结构和参数 11
结 论 13
参考文献 14
