Description: 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 neural networks:
1) Adaptive Linear Network (ADALINE)
2) Multilayer Layer Perceptron with Extended Backpropagation algorithm (EBPA)
3) Radial Basis Functions (RBF) Networks
4) RBF Networks with Extended Minimal Resource Allocating algorithm (EMRAN)
5) RBF and Piecewise Linear Networks with Dynamic Cell Structure (DCS) algorithm
A simulink example regarding the approximation of a scalar nonlinear function of 4 variables -Adaptive (Adaptive) The neural network source 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 five kinds of neural networks : a) Adaptive Linear Network (ADALINE) 2) 102206 with Multilayer Layer Extended Backpropagation algorithm (EBPA) 3) Radial Basis Functions (RBF) Networks, 4) RBF Networks with Extended Minimal Resource Allocating algorithm (EMRAN) 5) RBF and Piecewise Linear Dynamic Networks with the Cell Structure (DCS) algorithm A Simulink example regarding the approximation of a scalar nonlinear function of four variables Platform: |
Size: 200530 |
Author:周志连 |
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Description: 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) Adaptive Linear Network (ADALINE)
2) Multilayer Layer Perceptron with Extended Backpropagation algorithm (EBPA)
3) Radial Basis Functions (RBF) Networks
4) RBF Networks with Extended Minimal Resource Allocating algorithm (EMRAN)
5) RBF and Piecewise Linear Networks with Dynamic Cell Structure (DCS) algorithm
A simulink example regarding the approximation of a scalar nonlinear function of 4 variables is included-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 five kinds of neural networks : a) Adaptive Linear Network (ADALINE) 2) Multilayer Layer 102206 with Extended Backpropagation algorithm (EBPA) 3) Radial Basis Functions (RBF) Networks, 4) RBF Networks with Extended Minimal Resource Allocating algorithm (EMRAN) 5) and RBF Networks with Piecewise Linear Dynamic Cell Structure (DCS) algorithm A Simulink example regarding the approximation of a scalar nonlinear function of four variables is included Platform: |
Size: 198792 |
Author:叶建槐 |
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Description: 程序中存放结点值的数组和函数值的数组之所以命名为u和v，主要是为了防止和插值点ｘ，及对应的函数值单元y想混淆-process node storage array and the value of the function of the array has named u and v is mainly to prevent and interpolation points x, and the corresponding function modules y trying to confuse Platform: |
Size: 2048 |
Author:梁良 |
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Description: 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 neural networks:
1) Adaptive Linear Network (ADALINE)
2) Multilayer Layer Perceptron with Extended Backpropagation algorithm (EBPA)
3) Radial Basis Functions (RBF) Networks
4) RBF Networks with Extended Minimal Resource Allocating algorithm (EMRAN)
5) RBF and Piecewise Linear Networks with Dynamic Cell Structure (DCS) algorithm
A simulink example regarding the approximation of a scalar nonlinear function of 4 variables -Adaptive (Adaptive) The neural network source 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 five kinds of neural networks : a) Adaptive Linear Network (ADALINE) 2) 102206 with Multilayer Layer Extended Backpropagation algorithm (EBPA) 3) Radial Basis Functions (RBF) Networks, 4) RBF Networks with Extended Minimal Resource Allocating algorithm (EMRAN) 5) RBF and Piecewise Linear Dynamic Networks with the Cell Structure (DCS) algorithm A Simulink example regarding the approximation of a scalar nonlinear function of four variables Platform: |
Size: 200704 |
Author:周志连 |
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Description: 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) Adaptive Linear Network (ADALINE)
2) Multilayer Layer Perceptron with Extended Backpropagation algorithm (EBPA)
3) Radial Basis Functions (RBF) Networks
4) RBF Networks with Extended Minimal Resource Allocating algorithm (EMRAN)
5) RBF and Piecewise Linear Networks with Dynamic Cell Structure (DCS) algorithm
A simulink example regarding the approximation of a scalar nonlinear function of 4 variables is included-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 five kinds of neural networks : a) Adaptive Linear Network (ADALINE) 2) Multilayer Layer 102206 with Extended Backpropagation algorithm (EBPA) 3) Radial Basis Functions (RBF) Networks, 4) RBF Networks with Extended Minimal Resource Allocating algorithm (EMRAN) 5) and RBF Networks with Piecewise Linear Dynamic Cell Structure (DCS) algorithm A Simulink example regarding the approximation of a scalar nonlinear function of four variables is included Platform: |
Size: 198656 |
Author:叶建槐 |
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Description: ch5_1_1： 图像灰度线性变换 （§5.1.1）
ch5_1_2： 图像灰度分段线性变换 （§ 5.1.1）
ch5_1_3： 采用对数形式的变换函数进行动态范围压缩 （§5.1.1）
ch5_1_4： 图像直方图的均衡化 （§5.1.2）
ch5_1_5： 直方图规定化 （§5.1.2）
-ch5_1_1: image linear transformation (§ 5.1.1) ch5_1_2: piecewise linear image transform (§ 5.1.1) ch5_1_3: the use of logarithmic transformation of the form of dynamic range compression function (§ 5.1.1) ch5_1_4 : Image histogram equalization (§ 5.1.2) ch5_1_5: Histogram of the provisions of (§ 5.1.2) Platform: |
Size: 6144 |
Author:汤跃峰 |
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Description: 对于f(x)=1/(1+x^2) (-5<= x <=5)
要求选取11个等距插值节点，分别采用拉格朗日插值和分段线性插值，计算x为0.5, 4.5处的函数值并将结果与精确值进行比较。
输入：区间长度，n(即n+1个节点)，预测点
输出：预测点的近似函数值，精确值，及误差
-For f (x) = 1/(1+ x ^ 2) (-5 < = x < = 5) asked to select 11 equidistant interpolation nodes, respectively, using the Lagrange interpolation and piecewise linear interpolation to calculate x for the 0.5, 4.5 and the results of the function values compared with the accurate value. Input: interval length, n (ie n+1 nodes), the forecast points, Output: forecast of the approximate function value points, accurate values, and error Platform: |
Size: 1024 |
Author:loseheaven |
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Description: 这个程序是我用vc6.0写的分段线性分类器,用凹函数实现的,算法我觉的挺复杂,在看我的程序之前最好看一下清华大学出版社出版的模式识别中凹函数那一章.-This program, piecewise linear classifier, is written by me by using vc6.0, with the concave function implementation. I feel this algorithm a quite complex, please read concave function chapte of this book, Pattern Recognition, published by Tsinghua University Press. Platform: |
Size: 278528 |
Author:leon |
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Description: 用分段线性插值逼近函数f(x)
fsgdbbc -Approximation using piecewise linear interpolation function f (x) Platform: |
Size: 13312 |
Author:jiaqingyan |
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Description: This is a simple thing to do if you are willing to use a piecewise linear interpolant. More difficult is when the curve is a parametric spline or pchip model. The interparc.m function uses an ode solver to integrate the distance along the curve itself, then uses that ode solver to do the interpolation. Platform: |
Size: 6144 |
Author:zhenhao |
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Description: 非扭结样条程序
分段线性插值函数
含例题-Non-kink-spline interpolation function piecewise linear process with examples Platform: |
Size: 14336 |
Author:juchengyu |
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Description: a design methodology is introduced that
blends the classical PID and the fuzzy controllers in an
intelligent way and thus a new intelligent hybrid
controller has been achieved. Basically, in this design
methodology, the classical PID and fuzzy controller
have been combined by a blending mechanism that
depends on a certain function of actuating error.
Moreover, an intelligent switching scheme is induced
on the blending mechanism that makes a decision upon
the priority of the two controller parts namely, the
classical PID and the fuzzy constituents. The
simulations done on various processes using the new
hybrid fuzzy PID controller provides ‘better’ system
responses in terms of transient and steady-state
performances when compared to the pure classical PID
or the pure fuzzy controller applications. The controller
parameters are all tuned by the aid of genetic search
algorithm.-a design methodology is introduced that
blends the classical PID and the fuzzy controllers in an
intelligent way and thus a new intelligent hybrid
controller has been achieved. Basically, in this design
methodology, the classical PID and fuzzy controller
have been combined by a blending mechanism that
depends on a certain function of actuating error.
Moreover, an intelligent switching scheme is induced
on the blending mechanism that makes a decision upon
the priority of the two controller parts namely, the
classical PID and the fuzzy constituents. The
simulations done on various processes using the new
hybrid fuzzy PID controller provides ‘better’ system
responses in terms of transient and steady-state
performances when compared to the pure classical PID
or the pure fuzzy controller applications. The controller
parameters are all tuned by the aid of genetic search
algorithm. Platform: |
Size: 258048 |
Author:mohaideen |
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Description: 本文用1961-2006年广西88个气象观测站的各月雨量资料，北半球500百帕月平均高度场资料(576个格点)，国家气候中心提供的部分环流特征量资料，通过EOF分解、合成分析、S-A practical estimate on the credibility formula
is presented, where a piecewise linear function is taken as
the approximation of the prior distribution and applied to
the credibility theory. The convergence of the approximation
is analyzed. Platform: |
Size: 2048 |
Author:nongjifu |
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Description: 本文用1961-2006年广西88个气象观测站的各月雨量资料，北半球500百帕月平均高度场资料(576个格点)，国家气候中心提供的部分环流特征量资料，通过EOF分解、合成分析、S-A practical estimate on the credibility formula
is presented, where a piecewise linear function is taken as
the approximation of the prior distribution and applied to
the credibility theory. The convergence of the approximation
is analyzed. Platform: |
Size: 2048 |
Author:nongjifu |
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Description: 该程序利用分段线性函数对给定图像进行对比度调整。(This program performs contrast adjustment of given image using piecewise linear function) Platform: |
Size: 1701888 |
Author:fe15
|
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Description: 设计分段线性函数：转折点为(60,120)、(215,235)，对图像进行处理，并分析效果。(Design piecewise linear function: turning point is (60120), (215235), image processing, and analyze the effect.) Platform: |
Size: 84992 |
Author:王妍 |
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Search - piecewise linear function