文件名称:ffc-1.4.tar
- 所属分类：
- 人工智能/神经网络/遗传算法
- 资源属性：
- 上传时间：
- 2012-11-26
- 文件大小：
- 247kb
- 下载次数：
- 0次
- 提 供 者：
- ba***
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介绍说明－－下载内容均来自于网络，请自行研究使用
Key Features
* Neural network design, training, and simulation
* Pattern recognition, clustering, and data-fitting tools
* Supervised networks including feedforward, radial basis, LVQ, time delay, nonlinear autoregressive (NARX), and layer-recurrent
* Unsupervised networks including self-organizing maps and competitive layers
* 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
* Routines for improving generalization to prevent overfitting
* Simulink® blocks for building and evaluating neural networks, and advanced blocks for control systems applications-Key Features
* Neural network design, training, and simulation
* Pattern recognition, clustering, and data-fitting tools
* Supervised networks including feedforward, radial basis, LVQ, time delay, nonlinear autoregressive (NARX), and layer-recurrent
* Unsupervised networks including self-organizing maps and competitive layers
* 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
* Routines for improving generalization to prevent overfitting
* Simulink® blocks for building and evaluating neural networks, and advanced blocks for control systems applications
* Neural network design, training, and simulation
* Pattern recognition, clustering, and data-fitting tools
* Supervised networks including feedforward, radial basis, LVQ, time delay, nonlinear autoregressive (NARX), and layer-recurrent
* Unsupervised networks including self-organizing maps and competitive layers
* 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
* Routines for improving generalization to prevent overfitting
* Simulink® blocks for building and evaluating neural networks, and advanced blocks for control systems applications-Key Features
* Neural network design, training, and simulation
* Pattern recognition, clustering, and data-fitting tools
* Supervised networks including feedforward, radial basis, LVQ, time delay, nonlinear autoregressive (NARX), and layer-recurrent
* Unsupervised networks including self-organizing maps and competitive layers
* 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
* Routines for improving generalization to prevent overfitting
* Simulink® blocks for building and evaluating neural networks, and advanced blocks for control systems applications
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