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.
標簽： input-output the generated following
上傳時間： 2014-01-05
上傳用戶：royzhangsz
State_space_reconstruction_parameters_in_the_analysis_of_chaotic_time_series_-_the_role_of_the_time_window_length. It is used for reconstruction of state space in chaotic time series, and also how to determine time window.
標簽： State_space_reconstruction_parame ters_in_the_analysis_of_chaotic_t the_role_of_
上傳時間： 2013-12-21
上傳用戶：fandeshun
Nonlinear_dynamics_delay_times_and_embedding_windows. How to determine embedded window for chaotic state space of time series
標簽： Nonlinear_dynamics_delay_times_an d_embedding_windows determine embedded
上傳時間： 2016-02-21
上傳用戶：tianyi223
一個遺傳算法 這是一個非常簡單的遺傳算法源代碼，是由Denis Cormier (North Carolina State University)開發的，Sita S.Raghavan (University of North Carolina at Charlotte)修正。代碼保證盡可能少，實際上也不必查錯。對一特定的應用修正此代碼，用戶只需改變常數的定義并且定義“評價函數”即可。注意代碼 的設計是求最大值，其中的目標函數只能取正值；且函數值和個體的適應值之間沒有區別。該系統使用比率選擇、精華模型、單點雜交和均勻變異。如果用 Gaussian變異替換均勻變異，可能得到更好的效果。代碼沒有任何圖形，甚至也沒有屏幕輸出，主要是保證在平臺之間的高可移植性。讀者可以從ftp.uncc.edu, 目錄 coe/evol中的文件prog.c中獲得。要求輸入的文件應該命名為‘gadata.txt’；系統產生的輸出文件為‘galog.txt’。輸入的 文件由幾行組成：數目對應于變量數。且每一行提供次序——對應于變量的上下界。如第一行為第一個變量提供上下界，第二行為第二個變量提供上下界，等等。
上傳時間： 2013-12-20
上傳用戶：myworkpost
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.
標簽： filtering particle Blackwellised conditionall
上傳時間： 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 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.
標簽： Rauch-Tung-Striebel algorithm smoother which
上傳時間： 2016-04-15
上傳用戶：zhenyushaw
Special picture button, easy configure... release. You only need one picture for pressed and one for normal state.
上傳時間： 2014-08-26
上傳用戶：c12228
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.
標簽： Simulator simulator variables EKF-SLAM
上傳時間： 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 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
標簽： very application powerful problems
上傳時間： 2014-01-16
上傳用戶：wang5829
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
標簽： ore Neurological performing Artificial
上傳時間： 2013-12-26
上傳用戶：hongmo
