Description: 利用rbf神经网络对人体组织药业灌注这个系统的一个学习，可以到达模拟预测效果-rbf use neural networks to human tissue perfusion of the medicine of a learning system, can be reached simulation results Platform: |
Size: 2048 |
Author:刘海云 |
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Description: LMS 最小军方误差算法仿真
可用于人工神经网络和自适应滤波-military smallest error LMS algorithm can be used for simulation of artificial neural networks and adaptive filtering Platform: |
Size: 1024 |
Author:胡 |
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Description: 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 model parameters as unknowns. The derivation and details are presented in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Sequential Bayesian Estimation and Model Selection Applied to Neural Networks . Technical report CUED/F-INFENG/TR 341, Cambridge University Department of Engineering, June 1999. After downloading the file, type "tar -xf version2.tar" to uncompress it. This creates the directory version2 containing the required m files. Go to this directory, load matlab5 and type "smcdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters.
-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 model parameters as unknowns. The derivation and details are presented in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Sequential Bayesian Estimation and Model Selection Applied to Neural Networks . Technical report CUED/F-INFENG/TR 341, Cambridge University Department of Engineering, June 1999. After downloading the file, type "tar-xf version2.tar" to uncompress it. This creates the directory version2 containing the required m files. Go to this directory, load matlab5 and type "smcdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters.
Platform: |
Size: 16384 |
Author:徐剑 |
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Description: This demo nstrates 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 model parameters as unknowns. The derivation and details are presented in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Sequential Bayesian Estimation and Model Selection Applied to Neural Networks . Technical report CUED/F-INFENG/TR 341, Cambridge University Department of Engineering, June 1999. After downloading the file, type "tar -xf version2.tar" to uncompress it. This creates the directory version2 containing the required m files. Go to this directory, load matlab5 and type "smcdemo1". 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 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 model parameters as unknowns. The derivation and details are presented in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Sequential Bayesian Estimation and Model Selection Applied to Neural Networks . Technical report CUED/F-INFENG/TR 341, Cambridge University Department of Engineering, June 1999. After downloading the file, type "tar-xf version2.tar" to uncompress it. This creates the directory version2 containing the required m files. Go to this directory, load matlab5 and type "smcdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters. Platform: |
Size: 220160 |
Author:晨间 |
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Description: 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.
Platform: |
Size: 348160 |
Author:晨间 |
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Description: Matlab在人工神经网络计算机仿真中的应用。通过仿真实例表明，Matlab是进行人工神经网络计算机仿真的有力工具-Artificial Neural Network in Matlab computer simulation applications. The simulation examples show that, Matlab is a computer simulation of artificial neural networks a powerful tool for Platform: |
Size: 20480 |
Author:飞猫 |
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Description: BP神经网络已广泛应用于非线性建摸、函数逼近、系统辨识等方面，但对实际问题，其模型结构需由
实验确定，无规律可寻。简要介绍了利用 Matlab语言进行 BP网络建立、训练、仿真的方法及注意事项。 -BP neural network has been widely used in nonlinear modeling, function approximation, system identification, etc., but the practical problems, the model structure required by the experiment, if there is no law to be found. Briefly introduce the use of Matlab language BP networks, training, simulation methods and Cautions. Platform: |
Size: 93184 |
Author:覃亮朋 |
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Description: 用MATLAB仿真的BP神经网络，采用LM算法，训练出一个比较好的训练曲线，一块发上来共享！-MATLAB simulation with BP neural networks, the use of LM algorithm, to train a better training curve, a piece of hair up to share! Platform: |
Size: 2048 |
Author:余咏波 |
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Description: 以某区水文数据为例，在matlab7中构建BP神经网络，进行仿真预测。-Hydrological data in a certain district, for example, in matlab7 Building BP neural networks, simulation prediction. Platform: |
Size: 1024 |
Author:猪宝贝 |
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Description: ：人工神经网络中的BP网络模型在函数逼近、模式识别等领域得到了广泛的应用，但利用神经网络
解决实际问题时，经常涉及到大量的数值计算问题，而运用计算机高级语言编程对神经网络BP模型进行仿真和
辅助设计是件十分麻烦的事情，为了解决这个矛盾，Matlab工具箱中专fq编制了大量有关设计BP网络模型的工
具函数 本文分析了Matlab软件包中人工神经网络工具箱的有关BP网络的工具函数，并给出了部分重要工具函
数的实际应用．-: Artificial Neural Networks in the BP network model in function approximation, pattern recognition and other fields a wide range of applications, but the use of neural networks to solve practical problems, often involving a large number of numerical problems, the use of high-level computer programming language to the neural network BP model is a piece-aided design simulation and a very troublesome thing, in order to solve this contradiction, Matlab toolbox secondary fq compiled a large number of BP network model of the design of utility functions This paper analyzes the Matlab software package on artificial neural network toolbox BP network, utility functions, and gives some important tools for the practical application of the function. Platform: |
Size: 118784 |
Author:杨元龙 |
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Description: 内容包括:人工神经网络简介、单层前向网络及LMS学习算法、多层前向网络及BP学习算法、支持向录机及其学习算法、Hopfield
神经网络,随机神经网络及模拟退火算法、竟争神经网络和协同神纤网络。每章均给出了基于MATLAB的仿真实例以及练习。
-Contents include: Introduction to artificial neural networks, single-layer feedforward network and the LMS learning algorithm, multilayer feedforward network and the BP learning algorithm, support to the recording machine and its learning algorithm, Hopfield neural network, stochastic neural networks and simulated annealing algorithm, actually God of war and synergistic neural network fiber network. Given in each chapter of the simulation based on MATLAB and practice. Platform: |
Size: 6059008 |
Author:小龙 |
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Description: 本文将BP神经网络应用于车牌的自动识别，在简述BP神经网络的基础上，重点讨论了用BP神经网络方法对车牌照字符的识别，用MATLAB完成了对车牌照数字识别的模拟，最后得出实验结果，证明这种方法是高效-BP neural networks used in automatic license plate recognition, in brief, based on BP neural network, focused on the BP neural network method for vehicle license plate character recognition, using MATLAB completed a simulation of vehicle license plate number recognition, Finally, experimental results obtained show that this method is efficient Platform: |
Size: 3353600 |
Author:likun |
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Description: 支持向量机和BP神经网络虽然都可以用来做非线性回归，但它们所基于的理论基础不同，回归的机理也不相同。支持向量机基于结构风险最小化理论，普遍认为其泛化能力要比神经网络的强。为了验证这种观点，本文编写了支持向量机非线性回归的通用Matlab程序和基于神经网络工具箱的BP神经网络仿真模块，仿真结果证实，支持向量机做非线性回归不仅泛化能力强于BP网络，而且能避免神经网络的固有缺陷——训练结果不稳定。-SVM and BP neural networks, although non-linear regression can be used to do, but they are based on different theoretical basis, the return mechanism is not the same. SVM based on structural risk minimization theory, generally considered the generalization ability of neural networks than strong. To test this view, a support vector machine of this writing the general non-linear regression procedures and based on Matlab neural network toolbox of the BP neural network simulation module, the simulation results confirm that support vector machines do not only the generalization ability of non-linear regression in BP network, and neural networks to avoid the inherent shortcomings- the training results unstable. Platform: |
Size: 11264 |
Author: |
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Description: 本书重点介绍了ＭＡＴＬＡＢ６.５神经网络工具箱中各种神经网络模型及基本理论，以及各种神经网络模型的MATLAB仿真程序设计方法，提供了MATLAB中神经网络工具箱函数的详解，对图形用户界面、SIMULINK和自定义神经网络等内容也进行了简介。-This book focuses on the neural network toolbox MATLAB6.5 various neural network models and basic theory, and a variety of neural network model of the MATLAB simulation program design, provides a neural network toolbox in MATLAB functions Detailed, graphics user interface, SIMULINK and custom neural networks and other content have also been Introduction. Platform: |
Size: 24849408 |
Author:天民 |
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Description: 该软件包采用MATLAB和C混合编程的方法实现了计算智能界新发现的, 也是国际最流行的神经元算法, 即Spike-Timing Dependent Plasticity. 本人完全独立设计及编制了该软件包,并经大量计算机模拟实验验证, 得到了权威专家的认可. 该软件包实现了复杂的STDP算法, 且全部参数可调. 本人使用此软件包进行的实验结果已经发表在IEEE Transactions on Neural Networks, Neurocomputing, IJNS等国际权威刊物上. 请放心使用.
使用时, 在MATLAB命令行中敲入STDP即可.-The package uses the method of mixed programming of MATLAB and C neurons newly discovered computational intelligence community, but also the most popular algorithms, namely the Spike-Timing Dependent Plasticity I completely independent design and preparation of the package, and by the large number of computer simulation experiments, has been recognized by authoritative experts, I use this package, the experimental results have been published in the IEEE Transactions on Neural Networks, Neurocomputing, IJNS international authoritative publications, please feel free to use. Platform: |
Size: 21504 |
Author:杨志军 |
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Description: For those who spend most of their time working indoors, the indoor air quality (IAQ) could affect their working efficiency and health. This paper presents an intelligent proportional-integral-derivative (PID) controller for IAQ control. Different the traditional PID controller, this novel controller combined with Back-Propagation Neural Networks (BPNN) technology will regulate the PID parameters kp, ki, kd automatically. In the present study, the algorithm of the BPNN-based PID controller is first discussed in details, and the control performance is then tested by simulation using MATLAB. The difficulty in IAQ control is the existence of control disturbance, time delay and measurement errors. The results show that the combined control algorithm has better performance on the systemic stability, disturbance resistance, fast response rate and small overshoot compared with traditional PID controller.-For those who spend most of their time working indoors, the indoor air quality (IAQ) could affect their working efficiency and health. This paper presents an intelligent proportional-integral-derivative (PID) controller for IAQ control. Different the traditional PID controller, this novel controller combined with Back-Propagation Neural Networks (BPNN) technology will regulate the PID parameters kp, ki, kd automatically. In the present study, the algorithm of the BPNN-based PID controller is first discussed in details, and the control performance is then tested by simulation using MATLAB. The difficulty in IAQ control is the existence of control disturbance, time delay and measurement errors. The results show that the combined control algorithm has better performance on the systemic stability, disturbance resistance, fast response rate and small overshoot compared with traditional PID controller. Platform: |
Size: 4096 |
Author:payam/baban |
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Description: 包括最小二乘法、SVM、神经网络、1_k近邻法,DAmWEag参数在matlab环境中自动识别连通区域的大小,双向PCS控制仿真，多姿态，多角度，有不同光照,cJEVujz条件粒子图像分割及匹配均为自行编制的子例程，现代信号处理中谱估计在matlab中的使用。- Including the least squares method, the SVM, neural networks, 1 _k neighbor method, DAmWEag parameter Automatic identification in the matlab environment the size of the connected area, Two-way PCS control simulation, Much posture, multi-angle, have different light, cJEVujz condition Particle image segmentation and matching subroutines themselves are prepared, Modern signal processing used in the spectral estimation in matlab. Platform: |
Size: 6144 |
Author:xdbgpg |
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Description: 通过关于BP神经网络的基础知识点和若干案例的描述，对神经网络的MATLAB仿真进一步丰富和完善。-Through the BP neural network on the basis of knowledge points and a number of cases described in the MATLAB simulation of neural networks to further enrich and improve. Platform: |
Size: 15360 |
Author:李萌 |
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Description: MATLAB features a family of application-specific collections of
functions called toolboxes. These extend the MATLAB environment in order
to solve particular classes of problems. Areas in which toolboxes are available
include signal processing, control systems design, dynamic systems simulation,
systems identification, neural networks, and others. Platform: |
Size: 419840 |
Author:khang
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Search - simulation of NEURAL NETWORKS in matlab