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Description: 针对航空照片中自动目标识别的复杂性．该文提出了一种基于神经网络的自动目标识别算法，
算法包括检测和虚警排除两个阶段．检1删阶段是在整幅区域中i挂行快速搜寻．找出所有可能的目标 虚
警排障阶段是对检测阶段得到的结果进行进一步的验证， 在尽可能保留全部真目标的前提下，将伪目标
排除．实验结果证明了算法的可行性．
关键词 神经网络，航空照片， 自动目标识别-against aerial photographs Automatic Target Recognition of complexity. This paper presents a neural network-based automatic target recognition algorithm, the algorithm including the detection and false alarm ruled out in two stages. Frederick stage is a cut in the whole region i linked to fast-track the search. To identify all possible targets false alarm remove obstacles stage of the testing phase results further verification, reservations at all possible real targets on the premise that goal will be to exclude false. Experimental results demonstrate the feasibility of the algorithm. Keywords neural network, aerial photographs, automatic target recognition
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Size: 133911 |
Author: 陈文清 |
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Description: MULTIDIMENSIONAL SCALING in matlab by Mark Steyvers 1999
％needs optimization toolbox
％Modified by Bruce Land
％--Data via globals to anaylsis programs
％--3D plotting with color coded groups
％--Mapping of MDS space to spike train temporal profiles as described in
％Aronov, et.al. \"Neural coding of spatial phase in V1 of the Macaque\" in
％press J. Neurophysiology-MULTIDIMENSIONAL SCALING in Matlab by Mar 1999% k Steyvers needs optimization toolbox% M odified by Bruce Land% -- Data via globals to ana ylsis programs% -- 3D plotting with color coded groups% -- Mapping of MDS space to spike train te mporal profiles as described in% Aronov, et.al. "Neural coding of spatial phase in V1 of t he Macaque "in press J. Neurophysiology%
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Size: 2288 |
Author: 左贤君 |
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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
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Size: 658294 |
Author: Jallon |
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Description: 针对航空照片中自动目标识别的复杂性．该文提出了一种基于神经网络的自动目标识别算法，
算法包括检测和虚警排除两个阶段．检1删阶段是在整幅区域中i挂行快速搜寻．找出所有可能的目标 虚
警排障阶段是对检测阶段得到的结果进行进一步的验证， 在尽可能保留全部真目标的前提下，将伪目标
排除．实验结果证明了算法的可行性．
关键词 神经网络，航空照片， 自动目标识别-against aerial photographs Automatic Target Recognition of complexity. This paper presents a neural network-based automatic target recognition algorithm, the algorithm including the detection and false alarm ruled out in two stages. Frederick stage is a cut in the whole region i linked to fast-track the search. To identify all possible targets false alarm remove obstacles stage of the testing phase results further verification, reservations at all possible real targets on the premise that goal will be to exclude false. Experimental results demonstrate the feasibility of the algorithm. Keywords neural network, aerial photographs, automatic target recognition
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Size: 134144 |
Author: 陈文清 |
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Description: MULTIDIMENSIONAL SCALING in matlab by Mark Steyvers 1999
％needs optimization toolbox
％Modified by Bruce Land
％--Data via globals to anaylsis programs
％--3D plotting with color coded groups
％--Mapping of MDS space to spike train temporal profiles as described in
％Aronov, et.al. "Neural coding of spatial phase in V1 of the Macaque" in
％press J. Neurophysiology-MULTIDIMENSIONAL SCALING in Matlab by Mar 1999% k Steyvers needs optimization toolbox% M odified by Bruce Land%-- Data via globals to ana ylsis programs%-- 3D plotting with color coded groups%-- Mapping of MDS space to spike train te mporal profiles as described in% Aronov, et.al. "Neural coding of spatial phase in V1 of t he Macaque "in press J. Neurophysiology%
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Size: 2048 |
Author: 左贤君 |
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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
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Size: 658432 |
Author: Jallon |
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Description: 基于matlab完成的神经网络源程序，包含神经网络的初始化，学习阶段和训练阶段-Based on the completion of the neural network matlab source code, including initialization of neural networks, learning phase and training phase
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Size: 1024 |
Author: Aaron |
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Description: 针对实际对象数学模型不明确而难以控制的问题,采用人工免疫网络的离散模
型与学习算法,将人工免疫系统与神经网络结构的优势相结合,提出了一种基于人工免疫
网络的模式识别算法,构造了对象识别的人工免疫网络模型.该算法综合了网络节点的定
位与参数调整以及对基函数的平滑因子实施调谐等功能,有效地解决了径向基函数
(RBF)神经网络模式识别的两个阶段任务,使模式识别的精度有较大的改进.采用两个不
同对象函数进行的仿真试验表明,该算法具有快速收敛性与较高的准确性.
-Mathematical model for the actual object is not clear and difficult to control the problem, the use of artificial immune network model and the discrete learning algorithm, artificial immune systems and neural networks combine the advantages of structure, an artificial immune network based on pattern recognition algorithms, Construction of the object identified by artificial immune network model. the algorithm is a combination of network nodes to adjust the position and parameters as well as the basis function implementation of the smoothing factor, tuning and other functions, to effectively solve the Radial Basis Function (RBF) neural network pattern recognition of two phase of the task, so that the accuracy of pattern recognition have greater improvements. the use of two different object functions of the simulation tests show that the algorithm has fast convergence and higher accuracy.
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Author: 杨玉琴 |
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Description: 摘要：
本文研究了基于小世界结构的神经网络中的联想记忆模型。网络恢复存储模式的行为其实是无序参数为一有限值时的相位变化。越是规则的网络越是难以恢复记忆模式，且容易变成混合状态。另外，在无序参数的值适中时，对于一定数量的存储模式，最终得到恢复的效果可以达到最大。-Abstract: This paper studies the structure based on small-world neural network model of associative memory. Network storage mode to restore the behavior is a disorder parameter for the limited value of the phase change. The more rules the more difficult to restore the network memory model, and easy to become a mixed state. In addition, the value of the parameter in the disordered medium, the model for a certain amount of storage, and ultimately be restored to achieve the greatest effect.
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Size: 87040 |
Author: 范卫华 |
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Description: 由于本人近阶段在研究神经网络方面的，所以把有关方面的共享给大家。
这段是用rbf函数逼近的源码。可直接编译运行-Due to recent phase I study of neural networks, so the parties to share to everyone. This is the source function approximation rbf. Direct the compiler to run
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Size: 1024 |
Author: 张芳 |
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Description: Phase-based Opic 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.- Phase-based Opic 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.
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Size: 2048 |
Author: Deng Fu Qin |
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Description: 一种基于相位的光流计算方法，该方法不同于以往基于微分的计算方法，而是采用空间滤波器，取得非常好的效果。该结果发表于IEEE Trans. Neural Networks,13(5), 1127--1136.
-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.
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Size: 657408 |
Author: yishui pan |
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Description: 基于相位的光流估计，Trans NN 2002的文章-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
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Size: 656384 |
Author: 李 |
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Description: 1.BP神经网络进行模式识别
2.用BP网络对非线性系统进行辨识
3.一个神经网络PID控制器
4.图像处理的PCA算法
5.图像处理的穷举算法-1.BP neural network pattern recognition 2. Using BP network identification of nonlinear systems 3. A neural network PID controller 4. The PCA algorithm for image processing 5. The exhaustive algorithm for image processing,
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Size: 5120 |
Author: hudingyin |
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Description: This program is a simple demonstration of a Kohonen self-organizing neural network. The program merely maps itself to a set of coordinates ranging from -0.5 to 0.5 on both the x and y-axis.
The program layout is very simple - the Run button will start the network. Note, this may take some time, so be patient! Note that phase is shown in the title bar. The two edit controls at the right are the Phase 1 (top) and Phase 2 (bottom) iterations. Alter these values to see how it affects the learning of the program.
During Phase 1, the learning coefficient, k, goes from 0.9 to 0.01, linearly decreasing with the iterations. The neighbourhood, Nx is set at half the diameter of the net, and also linearly decreases. During Phase 2, k decreases from 0.1 to 0.0, with Nx fixed at 1.-This program is a simple demonstration of a Kohonen self-organizing neural network. The program merely maps itself to a set of coordinates ranging from-0.5 to 0.5 on both the x and y-axis.
The program layout is very simple- the Run button will start the network. Note, this may take some time, so be patient! Note that phase is shown in the title bar. The two edit controls at the right are the Phase 1 (top) and Phase 2 (bottom) iterations. Alter these values to see how it affects the learning of the program.
During Phase 1, the learning coefficient, k, goes from 0.9 to 0.01, linearly decreasing with the iterations. The neighbourhood, Nx is set at half the diameter of the net, and also linearly decreases. During Phase 2, k decreases from 0.1 to 0.0, with Nx fixed at 1.
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Size: 180224 |
Author: Luigi |
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Description: 通过采用神经网络中的Clipping方法和MonteCarlo修改学习算法，对用于光学模式识别的纯相位二值化匹配滤波器进行了优化设计。计算机模拟结果表明，和传统的纯相位匹配滤波器的相关输出结果相比，其识别输出的信噪比和信号相关峰值得到了明显的提高，从而为今后的光学实现奠定了良好的基础。-Through the use of neural network methods and MonteCarlo modify Clipping learning algorithm for optical pattern recognition of binary phase-only matched filter optimized design. Computer simulation results show that the traditional phase-only matched filter correlation output compared with the results, which identify the output signal to noise ratio and signal correlation peak has been significantly improved, so as to achieve the future optical laid a good foundation.
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Size: 323584 |
Author: 刘杰 |
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Description: 神经元mass模型tonic 放电例子，并给出了相图-a example of neural mass model in tonic firing, and give its phase portrait
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Size: 1024 |
Author: 徸斌豪 |
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Description: Multi-step-prediction of chaotic time series based
on co-evolutionary recurrent neural network
协同进化递归神经网络的多步混沌时间序列预测-This paper proposes a co-evolutionary recurrent neural network (CERNN) for the multi-step-prediction of chaotic
time series, it estimates the proper parameters of phase space reconstruction and optimizes the structure of recurrent
neural networks by co-evolutionary strategy. The searching space was separated into two subspaces and the individuals
are trained in a parallel computational procedure. It can dynamically combine the embedding method with the capability
of recurrent neural network to incorporate past experience due to internal recurrence. The eff ectiveness of CERNN is
evaluated by using three benchmark chaotic time series data sets: the Lorenz series, Mackey–Glass series and real-world
sun spot series. The simulation results show that CERNN improves the performances of multi-step-prediction of chaotic
time series.
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Description: ：基于脑电信号的身份识别是通过采集试验者的脑部信号来进行身份认证。对于同一个外部刺激或者主体在思考同一个
事件的时候，不同人的大脑所产生的认知脑电信号不同。选取与运动意识想象有关的电极后，分析不同个体在特定状况下脑
电的个体差异，采用以回归系数、能量谱密度、相同步、线性复杂度多种信号处理结合方法对运动想象脑电信号进行处理来
进行特征提取。组合多元特征向量并运用多层BP 神经网络对不同个体的脑电信号进行分类，并在不同的意识想象及不同数
据长度、不同的波段对试验者进行识别率验证分析。结果表明，不同运动想象的平均识别率均在80 以上，其中以想象舌头
运动的识别率较高，达到90.6 ，不同波段的识别率也表明意识想象的模式及相应波段对身份认别有较大的影响。-EEG-based identification to authenticate through the acquisition of experimental brain signals. For the same external stimuli, or the main thinking of the same
Event, different people s brains produced by cognitive EEG. Select imagine the electrodes and movement awareness, analysis of different individuals in a particular situation brain
Individual differences in electricity, the use of regression coefficients, the energy spectral density, phase synchronization, the linear complexity of a variety of signal processing combined with motor imagery EEG
For feature extraction. The combination of multiple feature vectors and the use of multi-layer BP neural network to classify the EEG signals of different individuals, and in a different sense of imagination and a different number of
Length, the band on the test to verify the analysis of the recognition rate. The results show that the average recognition rates of different motor imagery in more than 80 , which to imagine the tongue
The m
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Author: 王闯杰 |
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Description: 基于ADMC_401的三相交流感应电机的SVPWM变频调速研究，通过对感应电动机的矢量控制原理分析, 提出了一种PID神经元网络控制器-SVPWM variable frequency speed control studies based ADMC_401 three-phase AC induction motor, induction motor vector control principle analysis, proposed a PID neural network controller
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Author: x |
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