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State-transition

To estimate the input-output mapping with inputs x ％ and outputs y generated by the following nonli

To estimate the input-output mapping with inputs x ％ and outputs y generated by the following nonlinear, ％ nonstationary state space model: ％ x(t+1) = 0.5x(t) + [25x(t)]/[(1+x(t))^(2)] ％ + 8cos(1.2t) + process noise ％ y(t) = x(t)^(2) / 20 + 6 squareWave(0.05(t-1)) + 3 ％ + time varying measurement noise ％ using a multi-layer perceptron (MLP) and both the EKF and ％ the hybrid importance-samping resampling (SIR) algorithm.

標(biāo)簽： input-output the generated following

上傳時(shí)間： 2014-01-05

上傳用戶：royzhangsz
State_space_reconstruction_parameters_in_the_analysis_of_chaotic_time_series_-_the_role_of_the_time_

State_space_reconstruction_parameters_in_the_analysis_of_chaotic_time_series_-_the_role_of_the_time_window_length. It is used for reconstruction of state space in chaotic time series, and also how to determine time window.

標(biāo)簽： State_space_reconstruction_parame ters_in_the_analysis_of_chaotic_t the_role_of_

上傳時(shí)間： 2013-12-21

上傳用戶：fandeshun
Nonlinear_dynamics_delay_times_and_embedding_windows. How to determine embedded window for chaotic

Nonlinear_dynamics_delay_times_and_embedding_windows. How to determine embedded window for chaotic state space of time series

標(biāo)簽： Nonlinear_dynamics_delay_times_an d_embedding_windows determine embedded

上傳時(shí)間： 2016-02-21

上傳用戶：tianyi223
一個(gè)遺傳算法這是一個(gè)非常簡(jiǎn)單的遺傳算法源代碼

一個(gè)遺傳算法這是一個(gè)非常簡(jiǎn)單的遺傳算法源代碼，是由Denis Cormier (North Carolina State University)開(kāi)發(fā)的，Sita S.Raghavan (University of North Carolina at Charlotte)修正。代碼保證盡可能少，實(shí)際上也不必查錯(cuò)。對(duì)一特定的應(yīng)用修正此代碼，用戶只需改變常數(shù)的定義并且定義“評(píng)價(jià)函數(shù)”即可。注意代碼的設(shè)計(jì)是求最大值，其中的目標(biāo)函數(shù)只能取正值；且函數(shù)值和個(gè)體的適應(yīng)值之間沒(méi)有區(qū)別。該系統(tǒng)使用比率選擇、精華模型、單點(diǎn)雜交和均勻變異。如果用 Gaussian變異替換均勻變異，可能得到更好的效果。代碼沒(méi)有任何圖形，甚至也沒(méi)有屏幕輸出，主要是保證在平臺(tái)之間的高可移植性。讀者可以從ftp.uncc.edu, 目錄 coe/evol中的文件prog.c中獲得。要求輸入的文件應(yīng)該命名為‘gadata.txt’；系統(tǒng)產(chǎn)生的輸出文件為‘galog.txt’。輸入的文件由幾行組成：數(shù)目對(duì)應(yīng)于變量數(shù)。且每一行提供次序——對(duì)應(yīng)于變量的上下界。如第一行為第一個(gè)變量提供上下界，第二行為第二個(gè)變量提供上下界，等等。

標(biāo)簽： 算法源代碼

上傳時(shí)間： 2013-12-20

上傳用戶：myworkpost
The software implements particle filtering and Rao Blackwellised particle filtering for conditionall

The software implements particle filtering and Rao Blackwellised particle filtering for conditionally Gaussian Models. The RB algorithm can be interpreted as an efficient stochastic mixture of Kalman filters. The software also includes efficient state-of-the-art resampling routines. These are generic and suitable for any application. For details, please refer to Rao-Blackwellised Particle Filtering for Fault Diagnosis and On Sequential Simulation-Based Methods for Bayesian Filtering After downloading the file, type "tar -xf demo_rbpf_gauss.tar" to uncompress it. This creates the directory webalgorithm containing the required m files. Go to this directory, load matlab and run the demo.

標(biāo)簽： filtering particle Blackwellised conditionall

上傳時(shí)間： 2014-12-05

上傳用戶：410805624
In this demo, I use the EM algorithm with a Rauch-Tung-Striebel smoother and an M step, which I ve r

In this demo, I use the EM algorithm with a Rauch-Tung-Striebel smoother and an M step, which I ve recently derived, to train a two-layer perceptron, so as to classify medical data (kindly provided by Steve Roberts and Will Penny from EE, Imperial College). The data and simulations are described in: Nando de Freitas, Mahesan Niranjan and Andrew Gee Nonlinear State Space Estimation with Neural Networks and the EM algorithm After downloading the file, type "tar -xf EMdemo.tar" to uncompress it. This creates the directory EMdemo containing the required m files. Go to this directory, load matlab5 and type "EMtremor". The figures will then show you the simulation results, including ROC curves, likelihood plots, decision boundaries with error bars, etc. WARNING: Do make sure that you monitor the log-likelihood and check that it is increasing. Due to numerical errors, it might show glitches for some data sets.

標(biāo)簽： Rauch-Tung-Striebel algorithm smoother which

上傳時(shí)間： 2016-04-15

上傳用戶：zhenyushaw
Special picture button, easy configure... release. You only need one picture for pressed and one for

Special picture button, easy configure... release. You only need one picture for pressed and one for normal state.

標(biāo)簽： picture configure for one

上傳時(shí)間： 2014-08-26

上傳用戶：c12228
EKF-SLAM Simulator This version of the simulator uses global variables for all large objects, suc

EKF-SLAM Simulator This version of the simulator uses global variables for all large objects, such as the state covariance matrix. While bad programming practice, it is a necessary evil for MatLab efficiency, as MatLab has no facility to avoid gratuitous memory allocation and copying when passing (and modifying) variables between functions. With this concession, effort has been made to keep the code as clean and modular as possible.

標(biāo)簽： Simulator simulator variables EKF-SLAM

上傳時(shí)間： 2016-05-02

上傳用戶：lunshaomo
aiNet application is a very powerful and a very simple tool for solving the problems which are usual

aiNet application is a very powerful and a very simple tool for solving the problems which are usually solved with artificial neural networks (ANN). All possible tests we had run proved that the results obtained with aiNet are at least as good as the results obtained with some other ANNs. Let us state some of aiNet抯 features. (c) aiNet 1995-1997

標(biāo)簽： very application powerful problems

上傳時(shí)間： 2014-01-16

上傳用戶：wang5829
Ants performing 3 actions: searching ore , mining ore , returning ore basic Artificial Neurological

Ants performing 3 actions: searching ore , mining ore , returning ore basic Artificial Neurological Network working the learning proces to pick their state through Genetic Programming 人工智能中的蟻群算法

標(biāo)簽： ore Neurological performing Artificial

上傳時(shí)間： 2013-12-26

上傳用戶：hongmo

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