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
標簽： Neural collection implement Adaptive
上傳時間： 2013-12-23
上傳用戶：teddysha
常用的數學統計算法，希望大家喜歡 Calculate the APPROXIMATION of the standard normal distribution
標簽： 計算
上傳時間： 2014-01-08
上傳用戶：baiom
/* * EULER S ALGORITHM 5.1 * * TO APPROXIMATE THE SOLUTION OF THE INITIAL VALUE PROBLEM: * Y = F(T,Y), A<=T<=B, Y(A) = ALPHA, * AT N+1 EQUALLY SPACED POINTS IN THE INTERVAL [A,B]. * * INPUT: ENDPOINTS A,B INITIAL CONDITION ALPHA INTEGER N. * * OUTPUT: APPROXIMATION W TO Y AT THE (N+1) VALUES OF T. */
標簽： APPROXIMATE ALGORITHM THE SOLUTION
上傳時間： 2015-08-20
上傳用戶：zhangliming420
Electromagnetic scattering from the trees above a tilted rough ground plane generated by the stochastic Lidenmayer system is studied by Monte Carlo simulations in this paper.The scattering coefficients are calculated in three methods:coherent addition APPROXIMATION,tree-independent scattering,and independent scattering.
標簽： Electromagnetic scattering generated the
上傳時間： 2013-12-06
上傳用戶：xieguodong1234
When working with mathematical simulations or engineering problems, it is not unusual to handle curves that contains thousands of points. Usually, displaying all the points is not useful, a number of them will be rendered on the same pixel since the screen precision is finite. Hence, you use a lot of resource for nothing! This article presents a fast 2D-line APPROXIMATION algorithm based on the Douglas-Peucker algorithm (see [1]), well-known in the cartography community. It computes a hull, scaled by a tolerance factor, around the curve by choosing a minimum of key points. This algorithm has several advantages: 這是一個基于Douglas-Peucker算法的二維估值算法。
標簽： mathematical engineering simulations problems
上傳時間： 2013-12-20
上傳用戶：changeboy
The package includes 3 Matlab-interfaces to the c-code: 1. inference.m An interface to the full inference package, includes several methods for approximate inference: Loopy Belief Propagation, Generalized Belief Propagation, Mean-Field APPROXIMATION, and 4 monte-carlo sampling methods (Metropolis, Gibbs, Wolff, Swendsen-Wang). Use "help inference" from Matlab to see all options for usage. 2. gbp_preprocess.m and gbp.m These 2 interfaces split Generalized Belief Propagation into the pre-process stage (gbp_preprocess.m) and the inference stage (gbp.m), so the user may use only one of them, or changing some parameters in between. Use "help gbp_preprocess" and "help gbp" from Matlab. 3. simulatedAnnealing.m An interface to the simulated-annealing c-code. This code uses Metropolis sampling method, the same one used for inference. Use "help simulatedAnnealing" from Matlab.
標簽： Matlab-interfaces inference interface the
上傳時間： 2016-08-27
上傳用戶：gxrui1991
小波神經網絡的源程序: 1.構造的非線性函數: 位于nninit_test.m 2.直接用WNN逼近非線性:Wnn_test.m, (內部調用小波函數) 3.遺傳算法優化后逼近 :GA_Wnn_test.m (內部調用遺傳算法的,初始化,適應度,解碼函數)-genetic algorithm optimization WNN source : 1. Construction of the nonlinear function : nninit_test.m at 2. WNN directly with nonlinear APPROXIMATION : Wnn_test.m. (internal called wavelet function) 3. Genetic Algorithm optimization approach : GA_Wnn_test.m (internal called genetic algorithms, initialize, fitness and decoding functions)
標簽： nninit_test GA_Wnn_tes Wnn_test WNN
上傳時間： 2016-09-17
上傳用戶：LIKE
本人編寫的incremental 隨機神經元網絡算法，該算法最大的特點是可以保證APPROXIMATION特性，而且速度快效果不錯，可以作為學術上的比較和分析。目前只適合benchmark的regression問題。 具體效果可參考 G.-B. Huang, L. Chen and C.-K. Siew, “Universal APPROXIMATION Using Incremental Constructive Feedforward Networks with Random Hidden Nodes”, IEEE Transactions on Neural Networks, vol. 17, no. 4, pp. 879-892, 2006.
標簽： incremental 編寫 神經元網絡 算法
上傳時間： 2016-09-18
上傳用戶：litianchu
The main features of the considered identification problem are that there is no an a priori separation of the variables into inputs and outputs and the APPROXIMATION criterion, called misfit, does not depend on the model representation. The misfit is defined as the minimum of the l2-norm between the given time series and a time series that is consistent with the approximate model. The misfit is equal to zero if and only if the model is exact and the smaller the misfit is (by definition) the more accurate the model is. The considered model class consists of all linear time-invariant systems of bounded complexity and the complexity is specified by the number of inputs and the smallest number of lags in a difference equation representation. We present a Matlab function for approximate identification based on misfit minimization. Although the problem formulation is representation independent, we use input/state/output representations of the system in order
標簽： identification considered features separati
上傳時間： 2016-09-20
上傳用戶：FreeSky
The toolbox solves a variety of approximate modeling problems for linear static models. The model can be parameterized in kernel, image, or input/output form and the APPROXIMATION criterion, called misfit, is a weighted norm between the given data and data that is consistent with the model. There are three main classes of functions in the toolbox: transformation functions, misfit computation functions, and APPROXIMATION functions. The APPROXIMATION functions derive an approximate model from data, the misfit computation functions are used for validation and comparison of models, and the transformation functions are used for deriving one model representation from another. KEYWORDS: Total least squares, generalized total least squares, software implementation.
標簽： approximate The modeling problems
上傳時間： 2013-12-20
上傳用戶：15071087253
