Artificial Neural Network Theory

课程简介

从特性、类型、优点和缺点等方面介绍神经网络与软计算的基本原理。讨论生物神经元与神经系统，以及多种神经元模型、单层与多层网络、前馈与反馈网络和联想记忆。讲解多种有监督和无监督学习方法，如 delta 学习、反向传播、竞争学习、Hebb 学习、内容寻址、强化学习和循环学习。针对径向基函数网络、小波网络等多种网络形式讲解激活函数（传递函数），并介绍自适应共振理论，同时给出神经网络的应用实例。

The basic concepts of neural networks and soft computing in terms of characterization, categorization, advantages and disadvantages are introduced. Biological neurons and nervous systems are overviewed. Various models of neural such as single and multi layer networks, feed forward and feedback networks, associative memories are explained. Then, the learning rules (supervised and unsupervised) are described such as delta, back propagation, competitive, Hebbian, content addressable, reinforcement recurrent learning rule. Activation functions (transfer functions) are then explained in various models of networks for example radial basis function, wavelet function including adaptive resonance theory. Some neural applications are exemplified.