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Description: 用于定量的ANN,对于新手学人工神经网络的人来说因该不错,欢迎大家与我交流.-For quantitative ANN, for the novice learning artificial neural network for people because of the good, welcome you with my exchange.
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Size: 14336 |
Author: guorongdu |
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Description: ANN Simulink/Matlab. Constructing Kohonen Self-Organizing-Map (SOM)
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Size: 150528 |
Author: Pavigo |
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Description: 文件为《精通MATLAB神经网络》一书的源程序，文件中有大量用Simulink编写的源程序，对于学习如何用Simulink编写神经网络程序有帮助。-File " master MATLAB neural network," a book of the source, the document prepared by a large number of source code with Simulink, Simulink for learning how to write neural network programming help.
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Size: 610304 |
Author: sanmu |
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Description: 在simulink讲解了ANN的应用以及如何操作-In simulink explain the application and how to operate ANN
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Size: 411648 |
Author: 薛小鹏 |
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Description: AN ANN BASED APPROACH FOR
OPTIMAL PLACEMENT OF
DSTATCOM FOR VOLTAGE SAG
MITIGATION-Voltage sag has been considered as one of the most harmful power quality problem as it may significantly
affect industrial production. This paper presents an Artificial Neural Network (ANN) based approach for
optimal placement of Distribution Static Compensator (DSTATCOM) to mitigate voltage sag under
faults. Voltage sag under different type of short circuits has been estimated using MATLAB/SIMULINK
software. Optimal location of DSTATCOM has been obtained using a feed forward neural network
trained by post-fault voltage magnitude of three phases at different buses. Case studies have been
performed on IEEE 14-bus system and effectiveness of proposed approach of DSTATCOM placement
has been established
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Size: 452608 |
Author: 西蓝 |
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Description: 自适应神经网络图书馆（Matlab的5.3.1或更高版本）是实现多个自适应神经网络具有不同的自适应算法块的集合。
它主要分布于2001年6月 - 7月詹皮耶罗坎帕（西弗吉尼亚大学）和马里奥·卢卡Fravolini（佩鲁贾大学）。后来改善部分由美国航天局资助NCC5-685支持。
有迹象表明，实施基本上这些种神经网络的块：
自适应线性网络（ADALINE）
多层感知层网络
广义径向基函数网络
动力单元结构（DCS）网络的高斯或圆锥形基函数
此外，包括有关的标量非线性函数逼近的一个Simulink的例子。
最后，该文件包括Training.zip步步instrucions上如何培养GRBF网络和支撑例子。-Adaptive Neural Network Library (version 5.3.1 or higher Matlab' s) is to achieve a plurality of adaptive neural network has a different set of adaptive algorithms block. It is mainly distributed in June 2001- July 詹皮耶罗坎 Pa (West Virginia University) and Mario Luca Fravolini (Perugia University). Later improved in part by the NASA-funded NCC5-685 support. There are indications that the implementation of these types of neural networks basically block: adaptive linear network (ADALINE) multi-layer network-aware generalized radial basis function network power unit structure (DCS) network Gaussian or conical base function addition, including related Examples of scalar nonlinear function approximation of a Simulink. Finally, the document includes a step by step instrucions on how to cultivate GRBF Training.zip network and support examples.
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Size: 598016 |
Author: YuWang |
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