The Netlab toolbox is designed to provide the central tools necessary for the simulation of theoretically well founded neural network algorithms and related models for use in teaching, research and applications development. It contains many techniques which are not yet available in standard neural network simulation packages
標簽： simulation necessary the designed
上傳時間： 2013-12-11
上傳用戶：hj_18
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.
標簽： filtering particle Blackwellised conditionall
上傳時間： 2013-12-17
上傳用戶：zsjzc
一個非常好的時間序列工具箱，詳細使用說明見P. M. T. Broersen, Automatic Spectral Analysis with Time Series models, IEEE Transactions on Instrumentation and Measurement, Vol. 51, No. 2, April 2002, pp. 211-216.
上傳時間： 2014-01-14
上傳用戶：古谷仁美
ReBEL is a Matlabtoolkit of functions and scripts, designed to facilitate sequential Bayesian inference (estimation) in general state space models. This software consolidates research on new methods for recursive Bayesian estimation and Kalman filtering by Rudolph van der Merwe and Eric A. Wan. The code is developed and maintained by Rudolph van der Merwe at the OGI School of Science & Engineering at OHSU (Oregon Health & Science University).
標簽： Matlabtoolkit facilitate sequential functions
上傳時間： 2015-08-31
上傳用戶：皇族傳媒
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
HMMBOX, version 3.2, William Penny, Imperial College, Feb 1999 Matlab toolbox for Hidden Markov models
標簽： Imperial College version William
上傳時間： 2014-12-07
上傳用戶：515414293
VARHMMBOX, version 1.1, Iead Rezek, Oxford University, MAR 2002 Matlab toolbox for Hidden Markov models
標簽： University VARHMMBOX version toolbox
上傳時間： 2013-12-08
上傳用戶：rocketrevenge
Demostration of example 6.2: Constrained Receding Horizon Control Example retired from the book: Receding Horizon Control - Model Predictive Control for State models published on 2007-03-28
標簽： Demostration Constrained Receding Control
上傳時間： 2014-01-27
上傳用戶：330402686
The function conload takes a dataset and a model (PCA, PLS, PARAFAC etc.) and calculates congruence loadings which is the extension of correlation loadings to uncentered and multi-way models
標簽： calculates congruence and function
上傳時間： 2014-01-08
上傳用戶：sz_hjbf
The Staged Event-Driven Architecture (SEDA) is a new design for building scalable Internet services. SEDA has three major goals: To support massive concurrency, on the order of tens of thousands of clients per node To exhibit robust performance under wide variations in load and, To simplify the design of complex Internet services. SEDA decomposes a complex, event-driven application into a set of stages connected by queues. This design avoids the high overhead associated with thread-based concurrency models, and decouples event and thread scheduling from application logic. SEDA enables services to be well-conditioned to load, preventing resources from being overcommitted when demand exceeds service capacity. Decomposing services into a set of stages also enables modularity and code reuse, as well as the development of debugging tools for complex event-driven applications.
標簽： Event-Driven Architecture Internet building
上傳時間： 2015-09-28
上傳用戶：日光微瀾
