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icsiboost-0.3.tar
- Boosting is a meta-learning approach that aims at combining an ensemble of weak classifiers to form a strong classifier. Adaptive Boosting (Adaboost) implements this idea as a greedy search for a linear combination of classifiers by overweighting the
rls_estm
- This directory contains utility for implementing generic Reqursive Least Squares (RLS) algorithm. The example shows how one can use the utility to estamate the parameters of a simple linear discrete time system.
hopfieldNN
- matlab格式源代码。功能：HOPFIELD神经网络优化计算算法源码和应用于数字识别问题。-matlab source code format. Function: HOPFIELD neural network optimization algorithm applied to the digital source and identify the problem.
Robotswarm
- 用差分方法,进行机器人路径规划仿真的程序-Robot swarm simulation in discrete time. Builds on simulation of robot path planning problem for obstacle avoidance problem
shijianxulie
- 时间序列的时频特性分析研究时间序列的傅里叶变换及逆变换，快速梅林变换及逆变换，短时离散傅里叶变换，得到瞬时频率。 研究时间序列的Born–Jondan时频分布图，Butterworth时频分布图，Choi–Williams时频分布图，得到瞬时频率。 -Time series analysis of time-frequency characteristics of time series of Fourier transform and inverse transform, the fa
SVMhybridsystem
- A distributed PSOSVM hybrid system with feature selection and parameter optimization -Abstract This study proposed a novel PSO–SVM model that hybridized the particle swarm optimization (PSO) and support vector machines (SVM) to improve the clas
FUZZYITERATIVELEARNINGCONTROLDESINFOROUTPUT
- 模糊迭代学习控制的设计为输出 跟踪的离散模糊系统-FUZZY ITERATIVE LEARNING CONTROL DESIGN FOR OUTPUT TRACKING OF DISCRETE-TIME FUZZY SYSTEMS
Robot--obstacle-avoidance-problem
- Swarm仿真机器人在离散的时间。建立在机器人路径规划问题的仿真避障问题。-Robot swarm simulation in discrete time. Builds on simulation of robot path planning problem for obstacle avoidance problem.
introduction-of-adp
- 自适应动态规划介绍。一种求解动态规划方法HJB方程的自学习控制算法，称其为自适应动态规划算法。所提的算法可以用来解决未知离散时间非线性系统的最优控制问题，同时给出了该控制算法的收敛性证明。算法的实现用到了三个神经网络，在递推的每一步分别用来近似性能指标函数、最优控制律和未知非线性系统。-Adaptive Dynamic Programming introduction. Dynamic programming method for solving the HJB equation self-le
11122016
- Simulink Diagram and Code for Identifying a Discrete time Function using Neural Networks.