this demo is to show you how to implement a generic SIR (a.k.a. particle, bootstrap, Monte Carlo) filter to estimate the Hidden states of a nonlinear, non-Gaussian state space model.
標簽： a.k.a. bootstrap implement particle
上傳時間： 2014-11-10
上傳用戶：caozhizhi
CHMMBOX, version 1.2, Iead Rezek, Oxford University, Feb 2001 Matlab toolbox for max. aposteriori estimation of two chain Coupled Hidden Markov Models.
標簽： aposteriori University CHMMBOX version
上傳時間： 2014-01-23
上傳用戶：rocwangdp
madCollection 2.5.2.6 full source This is not your every day VCL component collection. You won t see many new colored icons in the component palette. My packages don t offer many visual components to play with. Sorry, if you expected that! My packages are about low-level stuff for the most part, with as easy handling as possible. To find the Hidden treasures, you will have to look at the documentation (which you re reading just in the moment). Later I plan on writing some nice demos, but for now the documentation must be enough to get you started.
標簽： madCollection collection component source
上傳時間： 2014-01-18
上傳用戶：yoleeson
Hidden_Markov_model_for_automatic_speech_recognition This code implements in C++ a basic left-right Hidden Markov model and corresponding Baum-Welch (ML) training algorithm. It is meant as an example of the HMM algorithms described by L.Rabiner (1) and others. Serious students are directed to the sources listed below for a theoretical description of the algorithm. KF Lee (2) offers an especially good tutorial of how to build a speech recognition system using Hidden Markov models.
標簽： Hidden_Markov_model_for_automatic speech_recognition implements left-right
上傳時間： 2016-01-23
上傳用戶：569342831
If you have programming experience and a familiarity with C--the dominant language in embedded systems--Programming Embedded Systems, Second Edition is exactly what you need to get started with embedded software. This software is ubiquitous, Hidden away inside our watches, DVD players, mobile phones, anti-lock brakes, and even a few toasters. The military uses embedded software to guide missiles, detect enemy aircraft, and pilot UAVs. Communication satellites, deep-space probes, and many medical instruments would have been nearly impossible to create without embedded software.
標簽： familiarity programming experience dominant
上傳時間： 2013-12-11
上傳用戶：362279997
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
Inside the C++ Object Model Inside the C++ Object Model focuses on the underlying mechanisms that support object-oriented programming within C++: constructor semantics, temporary generation, support for encapsulation, inheritance, and "the virtuals"-virtual functions and virtual inheritance. This book shows how your understanding the underlying implementation models can help you code more efficiently and with greater confidence. Lippman dispells the misinformation and myths about the overhead and complexity associated with C++, while pointing out areas in which costs and trade offs, sometimes Hidden, do exist. He then explains how the various implementation models arose, points out areas in which they are likely to evolve, and why they are what they are. He covers the semantic implications of the C++ object model and how that model affects your programs.
上傳時間： 2013-12-24
上傳用戶：zhouli
Batch version of the back-propagation algorithm. ％ Given a set of corresponding input-output pairs and an initial network ％ [W1,W2,critvec,iter]=batbp(NetDef,W1,W2,PHI,Y,trparms) trains the ％ network with backpropagation. ％ ％ The activation functions must be either linear or tanh. The network ％ architecture is defined by the matrix NetDef consisting of two ％ rows. The first row specifies the Hidden layer while the second ％ specifies the output layer. ％
標簽： back-propagation corresponding input-output algorithm
上傳時間： 2016-12-27
上傳用戶：exxxds
％ Train a two layer neural network with the Levenberg-Marquardt ％ method. ％ ％ If desired, it is possible to use regularization by ％ weight decay. Also pruned (ie. not fully connected) networks can ％ be trained. ％ ％ Given a set of corresponding input-output pairs and an initial ％ network, ％ [W1,W2,critvec,iteration,lambda]=marq(NetDef,W1,W2,PHI,Y,trparms) ％ trains the network with the Levenberg-Marquardt method. ％ ％ The activation functions can be either linear or tanh. The ％ network architecture is defined by the matrix NetDef which ％ has two rows. The first row specifies the Hidden layer and the ％ second row specifies the output layer.
標簽： Levenberg-Marquardt desired network neural
上傳時間： 2016-12-27
上傳用戶：jcljkh
Train a two layer neural network with a recursive prediction error ％ algorithm ("recursive Gauss-Newton"). Also pruned (i.e., not fully ％ connected) networks can be trained. ％ ％ The activation functions can either be linear or tanh. The network ％ architecture is defined by the matrix NetDef , which has of two ％ rows. The first row specifies the Hidden layer while the second ％ specifies the output layer.
標簽： recursive prediction algorithm Gauss-Ne
上傳時間： 2016-12-27
上傳用戶：ljt101007
