solution of TSP genetic algorithm C Programming Language

BP神经网络的C语言实现-Neural Network C Language

关于演化算法的小程序.以一个二元二次方程的求极值为例.对输入参数作了简化的固定处理.较好的说明了演化算法.可直接运行.-on evolutionary algorithm small procedures. A binary quadratic equation for the extreme example. To simplify the input parameters fixed address. A good note evolutionary algorithm. Direct operation.

遗忘因子法递推算法用于估计数学模型的参数,精确度较高!-Forgetting factor recursive algorithm method used to estimate the parameters of the mathematical model, high accuracy!

一个很好的bp神经网络预测程序实例，有较好的预测精确性-Bp a good example of neural network forecasting process, a better prediction accuracy

神经网络BP网络的计算程序-neural networks the computational procedures

Backpropagation Network Time-Series Forecasting 源码, 经典的BPN人工神经网络例子源码-Backpropagation Network Time-Series Forecasting source, the classic example of artificial neural network BPN source

基于机器学习的自动逻辑推理机。本程序用归结反演策略实现了命题逻辑系统的自动推理。把要解决的问题作为一个要证明的命题，其目标公式被否定并化成子句形，然后添加到命题公式集中去,把消解反演系统应用于联合集，并推导出一个空子句(NIL)，产生一个矛盾。-based on the automatic machine learning logical reasoning machine. The procedures used attributed inversion strategy to achieve the propositional logic of automatic reasoning. Put the problem to be solved as a proposition to prove, with the goal being negated formula and shape into a phrase and then added to the Proposition formula to concentrate, digestion system for joint inversion sets and derived an empty phrase (NIL), resulting in a contradiction.

基于matlab的改进模拟退火算法（经典）-Matlab-based improvements in simulated annealing algorithm (classical)

以黎曼几何为理论依据，基于S.Amari的修正核函数思想提出了两种新的保角变换，用其对核函数进行数据依赖性改进，进一步提高支持向量机分类器泛化能力。以人工非线性分类问题 为对象进行研究，仿真实验结果表明采用新保角映射可以快速显著地改善分类器泛化性能，而且能大幅度地减少支持向量的数目。-Two novel conformal transformations were proposed based on the Riemannian geometry theory and S.Amari’sidea.And the kernel function was modified by the transformation in a data-dependent way.Our experimental results for theartificial nonlinear data set show that the generalization performance of support vector machines classifier is improvedremarkably and the number of support vectors is decreased greatly.

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-The toolbox is designed to provide t he central tools necessary for the simulation o f theoretically well founded neural network al gorithms and related models for use in teaching , research and applications development. It c ontains many techniques which are not yet avail able in standard neural network simulation pac kages