MATLAB神经网络工具箱产品描述

翻译:石卓林 shizhuolin@hotmail.com

Neural Network Toolbox Product Description

Create, train, and simulate neural networks

Neural Network Toolbox™ provides functions and apps for modeling complex nonlinear systems that are not easily modeled with a closed-form equation. Neural Network Toolbox supports supervised learning with feedforward, radial basis, and dynamic networks. It also supports unsupervised learning with self-organizing maps and competitive layers. With the toolbox you can design, train, visualize, and simulate neural networks. You can use Neural Network Toolbox for applications such as data fitting, pattern recognition, clustering, time-series prediction, and dynamic system modeling and control.

To speed up training and handle large data sets, you can distribute computations and data across multicore processors, GPUs, and computer clusters using Parallel Computing Toolbox™.

Key Features

• Supervised networks, including multilayer, radial basis, learning vector quantization (LVQ), time-delay, nonlinear autoregressive (NARX), and layer-recurrent
• Unsupervised networks, including self-organizing maps and competitive layers
• Apps for data-fitting, pattern recognition, and clustering
• Parallel computing and GPU support for accelerating training (using Parallel Computing Toolbox)
• Preprocessing and postprocessing for improving the efficiency of network training and assessing network performance
• Modular network representation for managing and visualizing networks of arbitrary size
• Simulink® blocks for building and evaluating neural networks and for control systems applications

神经网络工具箱产品介绍

创建, 学习和模拟神经网络

神经网络工具箱™ 为复杂模型计算和非线性系统提供函数和应用,其并不是容易计算解的模式化闭式方程. 神经网络工具箱支持基于前馈网络, 径向基和动态网络的监督学习. 当然,它也支持基于自组织映射和竞争层的非监督学习. 使用此工具箱,你可以设计,训练,验证和和模拟神经网络. 可以使用神经网络工具箱的应用程序实现数据拟合,模式识别,聚类,时间序列预测和动态系统建模与管理.

为了提高训练速度和处理大数据, 可以使用分布计算和数据交叉多核处理器, GPU或使用并行计算工具™的计算群集.

关键特征

• 监督网络, 包含多层, 径向基, 学习向量量化 (LVQ), 时间延迟, 非线性自回归 (NARX), 层递归
• 非监督网络, 包含自组织映射和多层竞争
• 数据拟合,模式识别和聚类相关应用
• 并行计算和为加速学习提供GPU支持 (使用并行计算工具箱)
• 预处理和后处理用于提高网络训练与评估性能
• 任意大小的可管理的和可视化的模块表示
• Simulink® 模块用于构建和评估神经网络和其控制系统应用程序