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人工智能/神经网络 this demo is to show you how to implement a generic SIR (a.k.a. particle, bootstrap, Monte Carlo) fi
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.
数学计算 CHMMBOX, version 1.2, Iead Rezek, Oxford University, Feb 2001 Matlab toolbox for max. aposteriori e
CHMMBOX, version 1.2, Iead Rezek, Oxford University, Feb 2001
Matlab toolbox for max. aposteriori estimation of two chain
Coupled Hidden Markov Models.
Delphi/CppBuilder madCollection 2.5.2.6 full source This is not your every day VCL component collection. You won t se
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, wi ...
人工智能/神经网络 Hidden_Markov_model_for_automatic_speech_recognition This code implements in C++ a basic left-right
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 so ...
其他书籍 If you have programming experience and a familiarity with C--the dominant language in embedded syste
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 ...
人工智能/神经网络 本人编写的incremental 随机神经元网络算法
本人编写的incremental 随机神经元网络算法，该算法最大的特点是可以保证approximation特性，而且速度快效果不错，可以作为学术上的比较和分析。目前只适合benchmark的regression问题。
具体效果可参考
G.-B. Huang, L. Chen and C.-K. Siew, “Universal Approximation Using Incremental Constructive Feedforward Networ ...
软件设计/软件工程 Inside the C++ Object Model Inside the C++ Object Model focuses on the underlying mechanisms that s
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 bo ...
数值算法/人工智能 Batch version of the back-propagation algorithm. ％ Given a set of corresponding input-output pairs
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
...
人工智能/神经网络 ％ Train a two layer neural network with the Levenberg-Marquardt ％ method. ％ ％ If desired, it is p
％ 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,
％ ...
matlab例程 Train a two layer neural network with a recursive prediction error ％ algorithm ("recursive Gauss-Ne
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 , w ...