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自适应(Adaptive)神经网络源程序
The adaptive Neural Network Library is a collection of blocks that implement several Adaptive Neural Networks featuring
different adaptation algorithms.~..~
There are 11 blocks that implement basically these 5 kinds of
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The adaptive Neural Network Library is a collection of blocks that implement several Adaptive Neural Networks featuring
different adaptation algorithms.~..~
There are 11 blocks that implement basically these 5 kinds of neural networks:
1) A
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支持向量机的基本理论是从二类分类问题提出的，常用的核函数有：多项式、径向基、Sigmoid型。对于同一组数据选择不同的核函数，基本上都可以得到相近的训练效果。,Support vector machine' s basic theory is the question of second-class classification, commonly used kernel functions include: polynomial, radial basis, Sigmoid type. For
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基于径向基的神经网络程序。根据目标函数获得样本输入输出-Based on radial basis neural network procedures. According to the objective function to obtain a sample input and output
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ADIAL Basis Function (RBF) networks were introduced
into the neural network literature by Broomhead and
Lowe [1], which are motivated by observation on the local
response in biologic neurons. Due to their better
approximation capabilities, si
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document for radial function basis
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document for radial function basis implementation
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本文提出一种基于核方法的下视等分辨率景象匹配算法. 通过模拟电荷吸引模型, 提出了计算不等维高维数据相似度的SNN 核函数. 将图像中的特征点映射到径向基向量(Radial basis vector, RBV) 空间, 利用SNN 核函数计算两个特征点集的相似度及过渡矩阵. 利用置换测试模块来增强SNN 核的稳定性, 以确保输出解的可靠性. 实验证明, 基于SNN 核的景象匹配算法对图象畸变、噪声干扰与信号缺失具有很强的鲁棒性, 并可保证高精度与高实时性.
-This paper prese
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完善网络结构将RBF网络的径向基换成小波函数，调整权值和公式的变更，可望在仿真真结构中添加非奇异项以验证小波网络的辨识精度与能力，输入层加权值进行调整~..~
-Improve the network structure of RBF network of radial basis replaced by a wavelet function, adjust the weights and the formula change is expected to add a non-singula
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一种新的函数拟合方法，在某些问题的近似程度上甚至优于径向基插值和Kriging插值方法-A new function fitting method, even better than the approximate extent of some of the issues radial basis interpolation and Kriging interpolation method
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径向基函数神经网络Matlab源代码,非常实用-Radial basis functions are use for function approximation and interpolation. This package supports two popular classes of rbf: Gaussian and Polyharmonic Splines (of which the Thin Plate Spline is a subclass).
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基于RBF径向基核函数实现SVM支撑矢量机算法使用RBF，garma值为0.5-Based on RBF radial basis kernel function to achieve SVM support vector machine algorithm using Garma, RBF value of 0.5
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径向基（RBF）神经网络进行剩余寿命预测,绝对可运行！(Radial Basis Function (RBF) neural network to predict the remaining life)
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SURROGATES工具箱是一个多维函数逼近和优化方法的通用MATLAB库。当前版本包括以下功能：
实验设计：中心复合设计，全因子设计，拉丁超立方体设计，D-optimal和maxmin设计。
代理：克里金法，多项式响应面，径向基神经网络和支持向量回归。
错误和交叉验证的分析：留一法和k折交叉验证，以及经典的错误分析（确定系数，标准误差;均方根误差等;）。
基于代理的优化：高效的全局优化（EGO）算法。
其他能力：通过安全裕度进行全局敏感性分析和保守替代。(SURROGATES Toolbox
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径向基函数神经网络，基本算例.........(radial basis function)
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这是径向基函数神经网络的代码,在机器学习中有很强的应用性。(radial basis function)
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采用径向基神经网络搭建地下水预测模型，进行地下水预测。(The radial basis function neural network is used to build groundwater prediction model.)
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在matlab平台上实现RBF神经网络对辛烷值预测(Realization of RBFnet octane number prediction of radial basis function neural network on MATLAB platform)
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基于径向基神经网络的仓储机器人路径规划训练代码(Automated Guided Vehicle Path-panning Based on Radial Basis Function Neural Network)
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