搜索资源列表
rjMCMCsa
- On-Line MCMC Bayesian Model Selection This demo demonstrates how to use the sequential Monte Carlo algorithm with reversible jump MCMC steps to perform model selection in neural networks. We treat both the model dimension (number of neurons) and
PS0-SVR
- ：针对发酵过程中生物参数难以实时在线测量的问题，建立了用于生物参数状态预估的 支持向量机软测量模型。考虑到该支持向量回归(SVR)模型的复杂性和冷化特征取决于其三 个参数 ，c， 能否取到最优值，采用粒子群优化(PSO)算法实现对参数 ，c， 的同时寻优。在 此基础上，以饲料用 ．甘露聚糖酶为对象，建立了基于PSO—SVR的发酵过程产物浓度状态预估 模型。发酵罐控制结果表明：该模型具有很好的学习精度和泛化能力，可实现对 ．甘露聚糖酶 产物浓度的实时在线预估。-In
BP_neural_network_based_on-line_tuning_of_PID_para
- 论文《基于BP神经网络的参数在线整定的PID实时控制》-BP neural network based on-line tuning of PID parameters of real-time control
ANN-to-control-the-algorithm
- 基于BP神经网络的PID控制，利用神经网络的自学习、非线性和不依赖模型等特性实现PID参数的在线自整定，充分利用PID和神经网络的优点。-BP neural network based PID control, self-learning neural network, nonlinear and non-dependent model and other characteristics to achieve PID parameters on-line self-tuning, full us
fuzzy-PID
- 模糊PID控制器具有控制任意非线性函数的能力，能实现对PID控制器的参数Kp, Ki, Kd的实时在线整定，使系统具有更好的鲁棒性和自适应性，其输出也可以通过在线调整达到预期的控制精度。-Fuzzy PID controller has the ability to control an arbitrary nonlinear function, can achieve the parameters of the PID controller Kp, Ki, Kd,
Peucker
- 使用道格拉斯抽稀算法依据给定参数将折线进行抽稀以在尽量保证折线特点的前提下减少点的数量。-Using Douglas thinning algorithm based on the given parameters will be thinning line to try to ensure polyline features in the premise of reducing the number of points.
Fuzzy-Neural-Network-by-matlab
- 这是一个四个不同的S函数实现集合的递归模糊神经网络（RFNN）。该网络采用了4组可调参数,这使得它非常适合在线学习/操作，从而可应用到系统识别等方面。-This is a collection of four different S-function implementations of the recurrent fuzzy neural network (RFNN) described in detail in [1]. It is a four-layer, neuro-fuzzy net
psoRBFS110
- 用pso算法优化RBF神经网络，从而对微带线的S11参数进行建模-RBF neural networks were optimized by pso algorithm, thus model for S11 parameters of the microstrip line
0-svnn
- 这段代码实现了一个新的MLP神经网络训练方法,来自论文A new method for neural network regularization（内附）-This code implements a new training method for MLP neural networks, named Support Vector Neural Network (SVNN), proposed in the work: O. Ludwig “Study on Non-parametric Me
BP-PID
- 利用BP神经网络优化PID控制器参数，实现在线整定，达到最优化。(The parameters of PID controller are optimized by BP neural network to realize on-line tuning and optimization.)