Artificial Neural Networks
Artificial Neural Networks
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DOI：
10.1002/9781444324044.ch9
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发表时间：
2010-06
期刊：
影响因子：
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通讯作者：
Anil K. Jain;J. Mao;K. Mohiuddin
中科院分区：
文献类型：
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作者：
Anil K. Jain;J. Mao;K. Mohiuddin
人工神经网络（ANN）是从人类大脑的灵感。这些系统包含大量在并行架构中工作的神经元。每个神经元直接从系统或其他神经元获取输入。这些信息被处理并传递给其他神经元。这是使这些网络的所有简单和复杂问题解决能力成为可能的基本现象。本章讨论了神经网络的各种模型，包括多层感知器与反向传播算法，径向基函数网络，学习矢量量化，自组织映射和递归神经网络。我们讨论了这些网络的基本原理和解决问题的方法。大量的重点是各种系统参数及其在整个系统设计中的作用和重要性。我们进一步说明了不同模型的各种局限性。这就是我们在后面章节中介绍的使用混合动力系统的动机。
Artificial Neural Networks (ANN) are an inspiration from the human brain. These systems contain a large number of neurons that work in a parallel architecture. Each neuron takes its input directly from system or from other neurons. The information is processed and given to the other neurons. This is the basic phenomenon that makes possible all simple and complex problem solving ability of these networks. The chapter discusses the various models of neural networks that include multi-layer perceptron with back propagation algorithm, radial basis function networks, learning vector quantization, self organizing maps and recurrent neural networks. We discuss the basic philosophies and problem solving approach of these networks. A lot of emphasis is given on the various system parameters and their role and importance in the overall system design. We further illustrate the various limitations of the different models. This forms the motivation behind the use of hybrid systems that we present in the subsequent chapters.