Description: 这是一个好的建模学习资料,赶快下载吧,
数学建模十大算法 ( 包含:蒙特卡罗算法、数据拟合、参数估计、
插值等数据处理算法、线性规划、整数规划、多元规划、二次规划等规划类问题、
图论算法、动态规划、回溯搜索、分治算法、分支定界等计算机算法、
最优化理论的三大非经典算法：模拟退火法、神经网络、遗传算法、
网格算法和穷举法、一些连续离散化方法、数值分析算法、图象处理算法）-This a good model to study the information, download it quickly, mathematical modeling algorithm 10 (includes : Monte Carlo algorithm, data fitting, parameter estimation, interpolation of data-processing algorithms, linear programming, integer programming, diversity planning, Quadratic Programming category of zoning, graph-theoretical algorithms, dynamic programming, retroactive search, the partition algorithm, branch-and-bound algorithm computer, most optimization theory of the three non-classical algorithm : simulated annealing, neural networks, genetic algorithms, mesh algorithms and exhaustive, some consecutive discrete method, numerical analysis algorithms, image processing algorithm) Platform: |
Size: 5929399 |
Author:zhxj |
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Description: The code implements the optical flow algorithm described in Gautama, T. and Van Hulle, M.M. (2002). A Phase-based Approach to the Estimation of the Optical Flow Field Using Spatial Filtering,IEEE Trans. Neural Networks, 13(5), 1127--1136.
The algo proceeds in 3 steps
1. spatial filtering
2. phase gradient estimation
3. IOC using recurrent networks -The code implements the optical flow algor ithm described in Gautama, and T. Van Hulle. M.M. (2002). A Phase-based Approach to the Esti mation of the Optical Flow Field Using Spatial F iltering, IEEE Trans. Neural Networks, 13 (5), 1127 -- 1136. The algo proceeds in a three steps. spat ial filtering 2. 3 phase gradient estimation. I OC using recurrent networks Platform: |
Size: 658294 |
Author:Jallon |
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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.
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Size: 16422 |
Author:徐剑 |
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Description: In this demo, I use the EM algorithm with a Rauch-Tung-Striebel smoother and an M step, which I ve recently derived, to train a two-layer perceptron, so as to classify medical data (kindly provided by Steve Roberts and Will Penny from EE, Imperial College). The data and simulations are described in: Nando de Freitas, Mahesan Niranjan and Andrew Gee Nonlinear State Space Estimation with Neural Networks and the EM algorithm After downloading the file, type \"tar -xf EMdemo.tar\" to uncompress it. This creates the directory EMdemo containing the required m files. Go to this directory, load matlab5 and type \"EMtremor\". The figures will then show you the simulation results, including ROC curves, likelihood plots, decision boundaries with error bars, etc. WARNING: Do make sure that you monitor the log-likelihood and check that it is increasing. Due to numerical errors, it might show glitches for some data sets.
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Size: 198220 |
Author:晨间 |
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Description: 好用的。系统辨识中，递推最小二乘估计（RLS）是辨识模型阶次的一个重要的算法。该程序通过实现该算法，得到模型阶次的估计值以及相关参数值。
-refrain. System identification, estimation recursive least squares (RLS) identification model is of the order of an important algorithm. The procedures through the realization of the algorithm, to be the order of the model and estimated value of the relevant parameters. Platform: |
Size: 109568 |
Author:叶梭 |
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Description: The code implements the optical flow algorithm described in Gautama, T. and Van Hulle, M.M. (2002). A Phase-based Approach to the Estimation of the Optical Flow Field Using Spatial Filtering,IEEE Trans. Neural Networks, 13(5), 1127--1136.
The algo proceeds in 3 steps
1. spatial filtering
2. phase gradient estimation
3. IOC using recurrent networks -The code implements the optical flow algor ithm described in Gautama, and T. Van Hulle. M.M. (2002). A Phase-based Approach to the Esti mation of the Optical Flow Field Using Spatial F iltering, IEEE Trans. Neural Networks, 13 (5), 1127-- 1136. The algo proceeds in a three steps. spat ial filtering 2. 3 phase gradient estimation. I OC using recurrent networks Platform: |
Size: 658432 |
Author:Jallon |
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Description: 本程序实做MLP(Multi-layer perceptron)算法，使用者可以自行设定训练数据集与测试数据集，将训练数据集加载，在2、3维下可以显示其分布状态，并分别设定键节值、学习率、迭代次数来训练其类神经网络，最后可观看辨识率与RMSE（Root Mean squared error）来判别训练是否可以停止。-This procedure is to do MLP (Multi-layer perceptron) algorithm, the user can set their own training data set and test data sets, the training data set is loaded, in the 2,3-dimensional display of their distribution, and were set key section of the value of learning rate, number of iterations to train the neural network can watch the final recognition rate and the RMSE (Root Mean squared error) to determine whether the training can stop. Platform: |
Size: 1213440 |
Author:楊易 |
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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 MLP project in Neural Network that have userinterface. run GUI.m to execute project -this is MLP project in Neural Network that have userinterface. run GUI.m to execute project Platform: |
Size: 24576 |
Author:autstudent |
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Description: 在机动目标跟踪中，机动目标模型是机动目标跟踪的基本要素之一，一般希望机动目标模型能准确表征目标机动时的各种运动状态。比较常用的模型有匀速运动(CV)模型、匀加速运动(CA) 模型、时间相关模型（Singer）和机动目标“当前”统计模型。上述模型均采用机动频率表征目标的机动情况。在应用当中，通常采用固定的机动频率，这就表示机动目标的机动时间是一定的，而实际上机动目标的机动时间是不断变化的，也就是说机动频率是不断变化的，采用固定机动频率必然会带来误差。采样周期在0.5—2S时，机动频率越小跟踪精度越高[1]，但机动频率仍然是固定值。本文提出的基于神经网络的机动频率自适应调整方法可以使机动频率随机动而变化，从而提高状态估计的准确性，提高跟踪精度。本文将小波神经网络用于机动目标跟踪中机动频率的自适应调整，该算法对机动目标“当前”统计模型中的机动频率进行实时修改, 从而自适应的改变机动频率,使跟踪算法与目标的真实状态更接近。该算法采用小波神经网络的离线训练，实时性好。-The maneuver of the maneuvering target is uncertain. The maneuvering frequency is constantly changeable, but traditionally it is beforehand determined as a constant based on the target state estimation in the state model of the maneuvering target. The maneuver of the maneuvering target makes the kinematics equation of the target model mismatch with the practical motion model and the tracking error will be increased. Based on the advantages of the self-learning, the rapid convergence rate and the nonlinear approximation ability of the wavelet neural network, it was put forward to be used in the field of target tracking in the paper. The new residual is used as the input of the wavelet neural network, the output of the network is used to adjust adaptively the maneuvering frequency of the CS model. The algorithm is more close to the real state of the target. The simulation results showed that tracking error can be reduced and the tracking accuracy can be improved. Platform: |
Size: 4096 |
Author:李隆基 |
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Description: This paper describes a Model Reference Adaptive
System (MRAS) based scheme using a multilayer
Recurrent Neural Network (RNN) for online speed
estimation of sensorless vector controlled inductmon
motor drive.
Platform: |
Size: 11264 |
Author:chinni |
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Description: 著名的卡尔曼滤波器，植根于状态空间线性动力学系统，对线性递归最优滤波问题提出了一个叠代的解决方法。它不仅适用于平稳的环境，而且还适用于非平稳的环境。其估算结果是通过前一次的估计值与新的信息来计算更新的状态值；所以只有先前的估计值需要存储空间。卡尔曼滤波器采用了更高效的线性估计,比过去需要通过计算整个过滤过程中的每一个步骤更有效率。-The well-known Kalman filter, rooted in the state-space linear dynamical systems, an iterative solution of linear recursive optimal filtering problem. It applies not only to the stable environment, but also for non-stationary environment. The estimation result is calculated by the previous estimated value with the new information to update the state value require storage space, so that only the previously estimated values. The Kalman filter uses a more efficient linear estimation, than in the past need to be calculated in the entire filtration process every step more efficient. Platform: |
Size: 67584 |
Author:万达 |
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Description: the effects of neural
network aided estimation in such receivers are considered. Neural
network acts as a pre-processing block to the estimator.-Orthogonal frequency division multiplexing (OFDM) has
high data rate capacity and lower Inter Symbol Interference (ISI)
and is considered as the best solution for next generation mobile
communication. Multiple Inputs and Multiple Output (MIMO)
antenna system improve reception through spatial diversity and
high end coding. Combining these two, offers high interference
mitigation in wireless receivers. In this paper, the effects of neural
network aided estimation in such receivers are considered. Neural Platform: |
Size: 344064 |
Author:wangxx |
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Description: This paper presents neural networks based approach
for estimation of the control and operating parameters of Statcom
used for improving voltage profile in a power system, which is
emerging as a major problem in the day-to-day operation of
stressed power systems. Statcom is an important voltage source
converter FACTS device, which can be used in voltage control
mode or reactive power injection mode. For stable operation and
control of power systems it is essential to provide real time
solution to the operator in energy control centers. Artificial neural
networks are proposed here for this task, as they have ability to
synthesize complex mappings accurately and rapidly Platform: |
Size: 506880 |
Author:phdscolar11
|
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Description: 基于AlexNet网络模型的单幅彩色图的深度估计，在NYU Depth 数据集，Make3D 数据集，KITTI 数据集经过测试效果很好，只是本次上传由于大小限制，压缩包不包括数据集，读者可自行下载数据集进行训练！(Based on the AlexNet network model, the depth estimation of a single color map, in the NYU Depth dataset, Make3D dataset, KITTI dataset has been tested very well, but this upload due to size limitations, the compressed package does not include the dataset, the reader can Download the data set for training!) Platform: |
Size: 5120 |
Author:熊猫娃娃 |
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Description: OpenPose人体姿态识别项目是美国卡耐基梅隆大学（CMU）基于卷积神经网络和监督学习并以caffe为框架开发的开源库。可以实现人体动作、面部表情、手指运动等姿态估计。适用于单人和多人，具有极好的鲁棒性。是世界上首个基于深度学习的实时多人二维姿态估计应用，基于它的实例如雨后春笋般涌现。人体姿态估计技术在体育健身、动作采集、3D试衣、舆情监测等领域具有广阔的应用前景，人们更加熟悉的应用就是抖音尬舞机(OpenPost Human Attitude Recognition Project is an open source library developed by Carnegie Mellon University (CMU) based on convolutional neural network and supervised learning and caffe framework. Posture estimation such as human motion, facial expression and finger movement can be realized. It is suitable for single person and multi-person, and has excellent robustness. It is the first real-time multi-person two-dimensional attitude estimation application based on deep learning in the world. Examples based on it have sprung up like mushrooms after a spring rain. Human posture estimation technology has broad application prospects in sports fitness, motion acquisition, 3D fitting, public opinion monitoring and other fields. People are more familiar with the application of tremolo embarrassing dance machine.) Platform: |
Size: 45787136 |
Author:对对对对的 |
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Search - neural estimation