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Steep-descent

this is Demonstration of Wiener filter,LMS filter,Steep-descent algorithm.

this is Demonstration of Wiener filter,LMS filter,Steep-descent algorithm.

標(biāo)簽： filter Demonstration Steep-descent algorithm

上傳時間： 2013-12-09

上傳用戶：txfyddz
RLS,Demonstration of Wiener filter,LMS filter,Steep-descent algorithm

RLS,Demonstration of Wiener filter,LMS filter,Steep-descent algorithm

標(biāo)簽： filter Demonstration Steep-descent algorithm

上傳時間： 2016-05-24

上傳用戶：gououo
幾個matlab的實力

幾個matlab的實力，包括：非線性微分方程的求解.doc RLS,Demonstration of Wiener filter,LMS filter,Steep-descent algorithm.doc matlab下gabor濾波算法,可以提取圖象紋理特征.doc

標(biāo)簽： matlab

上傳時間： 2015-06-04

上傳用戶：lanhuaying
GDDEMO runs a little demonstration of gradient descent in Matlab. Launch Matlab, and type gddemo to

GDDEMO runs a little demonstration of gradient descent in Matlab. Launch Matlab, and type gddemo to get started.

標(biāo)簽： Matlab demonstration gradient descent

上傳時間： 2017-03-19

上傳用戶：Thuan
For the incomplete methods, we kept the representation of the queens by a table and the method of ca

For the incomplete methods, we kept the representation of the queens by a table and the method of calculation to determine if two queens are in conflict, which is much faster for this kind of problems than the representation by a matrix. heuristics: descent. Tests: 100 queens in less than 1 second and 67 iterations. 500 queens in 1 second and 257 iterations. 1000 queens in 11 seconds and 492 iterations. heuristics: Simulated annealing. Tests: 100 queens in less than 1 second and 47 iterations. 500 queens in 5 seconds and 243 iterations. 1000 queens in 13 seconds and 497 iterations. heuristics: based on Simulated Annealing. Tests: 100 queens in less than 1 second and 60 iterations. 500 queens in 1 second and 224 iterations. 1000 queens in 5 seconds and 459 iterations. 10 000 queens in 20 minutes 30 seconds and 4885 iterations.

標(biāo)簽： the representation incomplete methods

上傳時間： 2015-05-05

上傳用戶：1159797854
物流分析工具包。Facility location: Continuous minisum facility location, alternate location-allocation (ALA)

物流分析工具包。Facility location: Continuous minisum facility location, alternate location-allocation (ALA) procedure, discrete uncapacitated facility location Vehicle routing: VRP, VRP with time windows, traveling salesman problem (TSP) Networks: Shortest path, min cost network flow, minimum spanning tree problems Geocoding: U.S. city or ZIP code to longitude and latitude, longitude and latitude to nearest city, Mercator projection plotting Layout: Steepest descent pairwise interchange (SDPI) heuristic for QAP Material handling: Equipment selection General purpose: Linear programming using the revised simplex method, mixed-integer linear programming (MILP) branch and bound procedure Data: U.S. cities with populations of at least 10,000, U.S. highway network (Oak Ridge National Highway Network), U.S. 3- and 5-digit ZIP codes

標(biāo)簽： location location-allocation Continuous alternate

上傳時間： 2015-05-17

上傳用戶：kikye
％ COMPDIR Computes a search direction in a subspace defined by Z. ％ Helper function for NLCONST.

％ COMPDIR Computes a search direction in a subspace defined by Z. ％ Helper function for NLCONST. ％ Returns Newton direction if possible. ％ Returns random direction if gradient is small. ％ Otherwise, returns steepest descent direction. ％ If the steepest descent direction is small it computes a negative ％ curvature direction based on the most negative eigenvalue. ％ For singular matrices, returns steepest descent even if small.

標(biāo)簽： Z. direction Computes function

上傳時間： 2014-01-24

上傳用戶：Thuan
數(shù)值線性代數(shù)的Matlab應(yīng)用程序包共13個程序函數(shù)

數(shù)值線性代數(shù)的Matlab應(yīng)用程序包共13個程序函數(shù)，每個程序函數(shù)有相應(yīng)的例子函數(shù)一一對應(yīng)，以*Example.m命名程序名稱用途 Method 方法 GrmSch.m QR因子分解 classical Gram－Schmidt orthogonalization 格拉母－斯密特 MGrmSch.m QR因子分解 modified Gram－Schmidt iteration 修正格拉母－斯密特 householder.m QR因子分解 Householder 豪斯霍爾德QR因子分解 ZXEC.m 最小二乘擬合 polynomial interpolant 最小二乘插值多項式 NCLU.m LU因子分解 Gaussian elimination 不選主元素的高斯消元 PALU.m LU因子分解 partial pivoting Gaussian elimination 部分選主元的高斯消元 cholesky.m 楚因子分解 Cholesky Factorization 楚列斯基因子分解 PwItrt.m 求最大特征值 Power Iteration 冪迭代 Jacobi.m 求特征值 Jacobi iteration 按標(biāo)準(zhǔn)行方式次序的雅可比算法 Anld.m 求上Hessenberg Arnoldi Iteration 阿諾爾迪迭代 zuisu.m 解線性方程組 Steepest descent 最速下降法 CG.m 解線性方程組 Gradients 共軛梯度 BCG.m 解線性方程組 Biconjugate Gradients 雙共軛梯度

標(biāo)簽： Matlab 數(shù)值應(yīng)用程序函數(shù)

上傳時間： 2016-05-17

上傳用戶：小鵬
用于汽車巡航控制系統(tǒng)的模糊控制算法

用于汽車巡航控制系統(tǒng)的模糊控制算法，以及如何利用梯度下降法和卡爾曼濾波來優(yōu)化模糊控制器的算法。The files illustrate a simple fuzzy control algorithm as applied to an automobile cruise control system. The files also illustrate how gradient descent and Kalman filtering can be used to optimize the fuzzy controller .

標(biāo)簽： 汽車巡航控制系統(tǒng) 模糊控制算法

上傳時間： 2016-09-07

上傳用戶：xiaodu1124
BP神經(jīng)網(wǎng)絡(luò)非線性分類器

BP 神經(jīng)網(wǎng)絡(luò)的基本思想：信號的正向傳播+誤差的反向傳播。 ? 信號的正向傳播：輸入樣本從輸入層傳入，經(jīng)各隱層逐層處理后，傳向輸出層。 ? 誤差的反向傳播：將輸入誤差以某種形式通過隱層向輸入層逐層反傳，并將誤差分?jǐn)偨o各層的所有單元，從而獲得各層單元的誤差信號來作為修正各單元權(quán)值的依據(jù)。 BP算法屬于δ學(xué)習(xí)規(guī)則類，這類算法被稱為誤差的梯度下降（Gradient Descent）算法。在此分類器中，本文選擇3層BP神經(jīng)網(wǎng)絡(luò)算法。隱含層節(jié)點數(shù)為3。

標(biāo)簽： BP神經(jīng)網(wǎng)絡(luò) 非線性分類器

上傳時間： 2017-05-31

上傳用戶：jplalala

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Steep-descent

this is Demonstration of Wiener filter,LMS filter,Steep-descent algorithm.

RLS,Demonstration of Wiener filter,LMS filter,Steep-descent algorithm

幾個matlab的實力

GDDEMO runs a little demonstration of gradient descent in Matlab. Launch Matlab, and type gddemo to

For the incomplete methods, we kept the representation of the queens by a table and the method of ca

物流分析工具包。Facility location: Continuous minisum facility location, alternate location-allocation (ALA)

％ COMPDIR Computes a search direction in a subspace defined by Z. ％ Helper function for NLCONST.

數(shù)值線性代數(shù)的Matlab應(yīng)用程序包共13個程序函數(shù)

用于汽車巡航控制系統(tǒng)的模糊控制算法

BP神經(jīng)網(wǎng)絡(luò)非線性分類器