人工神经系统（ANSI，也称为神经网络）是一种尝试，旨在开发模拟生物神经系统（如人脑）的神经推理行为的计算机系统。因此，它们松散地基于生物神经网络。ANS由一系列节点（神经元）和加权连接（轴突）组成，当呈现特定的输入模式时，它们可以关联特定的输出模式。它本质上是一种高度复杂的非线性数学关系或变换。这些结构有两个重要特性，已被证明对信号处理和过程建模的作者有用: 噪声容忍和复杂模式识别。具体而言，作者开发了一种新的网络学习算法，成功地将ANS应用于高速信号处理和开发高度复杂过程的模型。本文讨论了其中的两个应用，即焊缝几何控制系统和焊接熔深监测系统。此外，作者还开发了一种齿轮监测系统的信号分析技术。这种系统在现场焊接电源系统的发动机健康监测中特别有用。

Artificial Neural System (ANSI, also known as neural network, is an attempt to develop computer systems that emulate the neural reasoning behavior of biological neural systems (e.g., the human brain). As such, they are loosely based on biological neural networks. The ANS consists of a series of nodes (neurons) and weighted connections (axons) that, when presented with a specific input pattern, can associate specific output patterns. It is essentially a highly complex, non-linear, mathematical relationship or transform. These constructs have two significant properties that have proven useful to the authors in signal processing and process modeling: noise tolerance and complex pattern recognition. Specifically, the authors have developed a new network learning algorithm that has resulted in the successful application of ANS to high speed signal processing and to developing models of highly complex processes. Two of the applications, the Weld Bead Geometry Control System, and Welding Penetration Monitoring System are discussed in the body of this paper. Additionally, the authors have developed a signal analysis technique for a gear monitoring system. Such a system would be particularly useful in the engine health monitoring of field welding power systems.