BP神经网络
来源:百度文库 编辑:神马文学网 时间:2024/05/15 09:14:57
BP(Back Propagation)网络是1986年由Rumelhart和McCelland为首的科学家小组提出,是一种按误差逆传播算法训练的多层前馈网络,是目前应用最广泛的神经网络模型之一。BP网络能学习和存贮大量的输入-输出模式映射关系,而无需事前揭示描述这种映射关系的数学方程。它的学习规则是使用最速下降法,通过反向传播来不断调整网络的权值和阈值,使网络的误差平方和最小。BP神经网络模型拓扑结构包括输入层(input)、隐层(hide layer)和输出层(output layer)。
Neural Network Toolbox
Design and simulate neural networks
- Introduction and Key Features
- Working with Neural Network Toolbox
- Network Architectures
- Training and Learning Functions
- Preprocessing and Postprocessing Functions
- Improving Generalization
- Simulink Support and Control Systems Applications
Technical Kit
- New Product Demonstration: Introduction to the Neural Network
- MATLAB based book by the authors of the Neural Network Toolbox: "Neural Network Design"
- Neural network design course offered via distance learning