FIR_rls

一种多目标优化算法的matlab程序，能够实现大量目标的同时优化，对多目标优化算法的学习很有帮助(a multi-object optimization matlab program,can optimize large object at meantime it will contribute to those who want to study muti-object optimization algorithm)

MATLAB实现多属性目标决策选择，从而选择最优的决策(MATLAB to achieve multi-attribute objective decision making the choice to select the optimal decisions)

目标分配的遗传算法在matlab的实现，可以用于在武器分配，以及背包问题。(Target allocation matlab genetic algorithm implementations that can be used in weapons distribution, and the knapsack problem.)

遗传算法-优化算法-模板-可以供学习参考(Genetic algorithms- Optimization- Template)

decompose the input images into subbands

用MATlab实现的遗传算法，i比较简单，适用于刚学习粒子群算法的人，大家可以交流一下(MATlab genetic algorithm, relatively simple, suitable for people new to the study of the particle swarm optimization (pso) algorithm, we can discuss)

Speech Recognition all source

This demo nstrates the use of the reversible jump MCMC algorithm for neural networks. It uses a hierarchical full Bayesian model for neural networks. This model treats the model dimension (number of neurons), model parameters, regularisation parameters and noise parameters as random variables that need to be estimated. The derivations and proof of geometric convergence are presented, in detail, in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Robust Full Bayesian Learning for Neural Networks. Technical report CUED/F-INFENG/TR 343, Cambridge University Department of Engineering, May 1999. After downloading the file, type "tar -xf rjMCMC.tar" to uncompress it. This creates the directory rjMCMC containing the required m files. Go to this directory, load matlab5 and type "rjdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters. (This demo nstrates the use of the reversible jump MCMC algorithm for neural networks. It uses a hierarchical full Bayesian model for neural networks. This model treats the model dimension (number of neurons), model parameters, regularisation parameters and noise parameters as random variables that need to be estimated. The derivations and proof of geometric convergence are presented, in detail, in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Robust Full Bayesian Learning for Neural Networks. Technical report CUED/F-INFENG/TR 343, Cambridge University Department of Engineering, May 1999. After downloading the file, type "tar-xf rjMCMC.tar" to uncompress it. This creates the directory rjMCMC containing the required m files. Go to this directory, load matlab5 and type "rjdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters. )

In this paper the reduction of Intersymbol Interference (1 ) in mobile communication channels is addressed. A well known technique for IS1 reduction is adaptive equalization in which the time-varying characteristics of the mobile channel is tracked by an adaptive algorithm. We compare different adaptive strategies and equalizer structures with respect to overall system performance in terms of computational complexity, numerical stability, finite wordlength effects, size and power consumption required for VLSI implementation.

simulation code for decode and forward