Description: 自组织系统Kohonen网络模型。对于Kohonen神经网络，竞争是这样进行的：对于“赢”的那个神经元c，在其周围Nc的区域内神经元在不同程度上得到兴奋，而在Nc以外的神经元都被抑制。网络的学习过程就是网络的连接权根据训练样本进行自适应、自组织的过程，经过一定次数的训练以后，网络能够把拓扑意义下相似的输入样本映射到相近的输出节点上。网络能够实现从输入到输出的非线性降维映射结构：它是受视网膜皮层的生物功能的启发而提出的。~..~-Kohonen network model. For Kohonen neural network, competition is this : For the "winner" of neurons c, in its switching around the region neurons in varying degrees, to be excited, and the switching outside the neurons were inhibited. Network learning is a process in the network connecting the right under the training samples for adaptive, self-organizing process, after a certain number of training, network topology can sense similar to the mapping of the input samples similar to the output nodes. Network can be achieved from input to output of nonlinear reduced-dimensional mapping structure : it is subject to retinal cortex of the biological function inspired by. ~ ~ .. Platform: |
Size: 34816 |
Author:张洁 |
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Description: 基于C开发的三个隐层神经网络，输出权值、阈值文件，训练样本文件，提供如下函数：1）初始化权、阈值子程序；2）第m个学习样本输入子程序；3）第m个样本教师信号子程序；4）隐层各单元输入、输出值子程序；5）输出层各单元输入、输出值子程序；6）输出层至隐层的一般化误差子程序；7）隐层至输入层的一般化误差子程序；8）输出层至第三隐层的权值调整、输出层阈值调整计算子程序；9）第三隐层至第二隐层的权值调整、第三隐层阈值调整计算子程序；10）第二隐层至第一隐层的权值调整、第二隐层阈值调整计算子程序；11）第一隐层至输入层的权值调整、第一隐层阈值调整计算子程序；12）N个样本的全局误差计算子程序。-C development based on the three hidden layer neural network, the output weights, threshold documents, training sample documents, for the following functions : a) initialization, the threshold subroutine; 2) m learning samples imported subroutine; 3) m samples teachers signal Subroutine ; 4) hidden layer of the module input and output value subroutine; 5) the output layer of the module input and output value subroutine; 6) the output layer to the hidden layer subroutine error of generalization; 7) hidden layer to the input layer subroutine error of generalization; 8) the output layer to the third hidden layer Weight adjustment, the output layer threshold adjustment routines; 9) 3rd hidden layer to the second hidden layer weights adjustment, the third hidden layer threshold adjustment routi Platform: |
Size: 11264 |
Author:李洋 |
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Description: 对具有随机噪声的二阶系统的模型辨识，进行标幺化以后系统的参考模型差分方程为： y(k)=a1*y(k-1)+a2*y(k-2)+b*u(k-1)+s(k) 式中，a1=0.3366,a2=0.6634,b=0.68,s(k)为随机噪声。由于神经网络的输出最大为1，所以，被辨识的系统应先标幺化，这里标幺化系数为5。采用正向建模(并联辨识)结构，神经网络选用3-9-9-1型，即输入层i，隐层j包括2级，输出层k的节点个数分别为3、9、9、1个；由于神经网络的最大输出为1，因此在辨识前应对原系统参考模型标么化处理，辨识结束后再乘以标么化系数才是被辨识系统的辨识结果。-of random noise with the second-order system model, per-unit system after the reference model differential equation : y (k) = y* a1 (k-1) a2* y (k-2) b* u (k-1) s (k)- style, = 0.3366 a1, a2 = 0.6634, b = 0.68, s (k) as random noise. Because the neural network for a maximum output, therefore, the identification system should be per-unit, per-unit here coefficient of 5. Forward modeling (Parallel identification) structure, neural network-based selection 3-9-9-1, i input layer, hidden layer, including two j, k output layer to the number of nodes 3,9,9,1; The neural network the biggest losers up to one, in the original deal before Identification System Reference Model S Mody treatment, then multiplied by the end of Identification Standard Mody coefficient was recognition system is the ide Platform: |
Size: 874496 |
Author:孙荣超 |
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Description: 误差反向传播网络(Back propagation network,简称BP网络)是神经网络中最活跃的方法，且绝大多数采用了三层结构(输入层、一个隐含层和输出层).BP网络是一种非线性映射人工神经网络.本程序用vb实现的bp算法-error back-propagation network (Back propagation network, called BP) neural network is the most active, but the majority adopted a three-tier structure (input layer, a hidden layer and output layer). BP network is a non-linear mapping of artificial neural networks. The procedures used vb the algorithm to achieve bp Platform: |
Size: 28672 |
Author:fuyu |
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Description: 神经网络训练根据Kolmogorov定理，输入层有14个节点，所以中间层有29个节点
％中间层神经元的传递函数为 tansig
％输出层有8个节点，其神经元传递函数为logsig
％训练函数采用traingdx-neural network training under the Kolmogorov theorem, input layer has 14 nodes, Therefore, the intermediate layer has 29% of nodes middle layer neurons in the transfer function for the output layer tansig% have eight nodes, its neuron transfer function for the training function logsig% used traingdx Platform: |
Size: 1024 |
Author:陈胜 |
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Description: 一个可以运行的som神经网络程序，可以任意输入输入和输出向量数。用于分类和测试-one can run the som neural network program, which could be imported input and output vectors of a few. For the classification and testing Platform: |
Size: 23552 |
Author:周君 |
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Description: matlab编程，通过设计训练一个人工神经网络来达到其对一个4传感器实时检测输出结果融合判断的模拟，文中涉及人工圣经网络和多传感器融合两们学科的基本知识。-matlab programming, through the design of training an artificial neural network to achieve its real-time detection of a 4-sensor integration to determine the output of the simulation, the text of the Bible involved in the artificial network and multi-sensor fusion of two disciplines have a basic knowledge. Platform: |
Size: 6144 |
Author:lingling84 |
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Description: 每一个训练范例在网络中经过两遍传递计算：一遍向前传播计算，从输入层开始，传递各层并经过处理后，产生一个输出，并得到一个该实际输出和所所需输出之差的差错矢量；一遍向反向传播计算，从输出层至输入层，利用差错矢量对权值进行逐层修改。BP算法有很强的数学基础，戏剧性地扩展了神经网络的使用范围，产生了许多应用成功的实例，对神经网络研究的再次兴起过很大作用。
-Each training example in the network passing through the calculation twice: once to move the spread of computing, from the beginning of input layer, transmission floors and processed to produce an output, and a the actual output and the difference between the desired output error vector again to reverse the spread of computing, from the output layer to the input layer, using error vector values of the right to amend the layers. BP algorithm has a strong mathematical foundation, dramatically expanded the use of neural networks, resulting in a number of successful examples of application of neural network research have a significant role in the rise once again. Platform: |
Size: 1024 |
Author:军军 |
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Description: 该代码实现了一中具有补偿的模糊神经网络结构，通过对一个函数的仿真，可以看出，该结构相应快速，输出稳定-The code to achieve a compensation with fuzzy neural network structure, a function of the simulation, we can see that the structure of the corresponding fast, stable output Platform: |
Size: 3072 |
Author:潇布衣 |
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Description: Train a two layer neural network with a recursive prediction error
％ algorithm ("recursive Gauss-Newton"). Also pruned (i.e., not fully
％ connected) networks can be trained.
％
％ The activation functions can either be linear or tanh. The network
％ architecture is defined by the matrix NetDef , which has of two
％ rows. The first row specifies the hidden layer while the second
％ specifies the output layer.-Train a two layer neural network with a recursive prediction error algorithm ( recursive Gauss-Newton ). Also pruned (ie, not fully connected) networks can be trained. The activation functions can either be linear or tanh. The network architecture is defined by the matrix NetDef, which has of two rows. The first row specifies the hidden layer while the second specifies the output layer. Platform: |
Size: 3072 |
Author:张镇 |
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Description: 基于BP神经网络识别字符.
BP神经网络算法是把一组样本输入输出问题转化为一个非线性优化问题，并通过梯度算法利用迭代运算求解权值的一种学习方法。采用BP网络进行分类，并附加线性感知器来实现单字符的有效识别，算法简便，识别率高，可适用于多种高噪声环境中的印刷体字符识别。-BP neural network based character recognition. BP neural network algorithm is a set of sample input and output is transformed into a nonlinear optimization problem, and through the use of iterative gradient algorithm for computing the value of a solution of the right way of learning. BP network used for classification, and additional linear perceptron to achieve an effective single-character recognition, the algorithm is simple, a high recognition rate, applicable to a wide range of high-noise environments print character recognition. Platform: |
Size: 113664 |
Author:吕寿鹏 |
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Description: ADIAL Basis Function (RBF) networks were introduced
into the neural network literature by Broomhead and
Lowe [1], which are motivated by observation on the local
response in biologic neurons. Due to their better
approximation capabilities, simpler network structures and
faster learning algorithms, RBF networks have been widely applied in many science and engineering fields. RBF network is three layers feedback network, where each hidden unit implements a radial activation function and each output unit implements a weighted sum of hidden units’ outputs. Platform: |
Size: 114688 |
Author:u123xz |
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Description: 倒立摆是一种复杂、时变、非线性、强耦合、自然不稳定的高阶系统，许多抽象的控制理论概念都可以通过倒立摆实验直观的表现出来。基于人工神经网络BP算法的倒立摆小车实验仿真训练模型，其倒立摆BP网络为4输入3层结构。输入层分别为小车的位移和速度、摆杆偏离铅垂线的角度和角速度。隐含层单元数16个。输出层设置为1个输出单元。输入层采用Tansig函数，隐含层采用Logsig函数，输出层采用Purelin函数。用Matlab 6.5数值计算软件对模型进行学习训练，并与线性反馈控制逻辑算法对比，表明倒立摆控制BP算法精度高、收敛快，在非线性控制、鲁棒控制等领域具有良好的应用前景。 -Inverted pendulum is a complex, time-varying, nonlinear, strong coupling, the natural instability of the high-end systems, many of the abstract concept of control theory to pass through the inverted pendulum experiment demonstrated intuitive. Based on artificial neural network BP algorithm inverted pendulum experiment simulation training model car, the Inverted Pendulum BP network input 3-layer structure of 4. Input layer, respectively, for the car s displacement and speed of deviation from the plumb line placed under the angle and angular velocity. Hidden layer unit number 16. Output layer is set to an output unit. Tansig function using input layer, hidden layer Logsig function used, the output layer Purelin function. Numerical calculation using Matlab 6.5 software for learning and training model, and linear feedback control logic algorithm comparison, show that the inverted pendulum control of BP algorithm and high precision, fast convergence in nonlinear control, robust control and Platform: |
Size: 217088 |
Author:月到风来AA |
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Description: 用神经网络中的BP网络来解决“异或问题”，异或函数在两个输入不等的情况下输出1，相等的情况输出0。-BP neural network with the network to solve the " exclusive-OR problem," the two input exclusive-OR function in the case of output ranging from 1, equal to the output of 0. Platform: |
Size: 1024 |
Author:zhangjin |
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Description: ：为提高移动机器人在未知环境下避障行为的成功率，通过对障碍物信息的输人，从控制输出数据中找出避
障行为模式，生成相应的模糊逻辑控制规则，并把模糊控制算法引入到神经网络中，使得模糊控制器规则的在线精
度和神经网络的学习速度均有较大的提高，使移动机器人具有较为迅速的反应能力，实现机器人连续、快速地避障
并最终到达目标．系统仿真证明了模糊神经网络在移动机器人路径选择中的智能性．-To enable the mobile robot in unknown environment obstacle—avoiding behavior of
Success，based on the information input of obstacles and from the control of output data to find a
obstacle—avoiding behavior model，and To create the fuzzy logic rules，a fuzzy control algorithm is
introduced to the neural network，allowing mobile robot more rapid response ability and to
achieve a robot， and finally reach the target of obstacle avoidance． system sim ulation proves a
fuzzy neural network in mobile robot path choice of intelligence． Platform: |
Size: 290816 |
Author:王风 |
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Description: 模糊神经网络设计，应用神经网络的一个实例 输入为－两输入，输出为－单输出-Fuzzy neural network design, an example of neural network input- two input, output- single output Platform: |
Size: 2048 |
Author:祁峥东 |
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Description: This "classic" style of neural network is used for spontaneously generating an algorithm for the analysis of numeric patterns. The goal is to "teach" the network how to analyze a desired set of numeric inputs. For instance, the network might be taught to calculate the AND, OR, and XOR of two input numbers: given the inputs of 0 and 1, the network should output 0, 1, and 1. The trick is that the network is not taught how to analyze the numbers the network is given several sets of inputs and the correct output for each input set, and attempts to synthesize an algorithm to provide correct outputs. If this process is correctly performed, the network may be able to yield the correct analysis of input sets for which it has not been taught the correct output.-This "classic" style of neural network is used for spontaneously generating an algorithm for the analysis of numeric patterns. The goal is to "teach" the network how to analyze a desired set of numeric inputs. For instance, the network might be taught to calculate the AND, OR, and XOR of two input numbers: given the inputs of 0 and 1, the network should output 0, 1, and 1. The trick is that the network is not taught how to analyze the numbers the network is given several sets of inputs and the correct output for each input set, and attempts to synthesize an algorithm to provide correct outputs. If this process is correctly performed, the network may be able to yield the correct analysis of input sets for which it has not been taught the correct output. Platform: |
Size: 56320 |
Author:diana |
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Description: 网络将输入模式加权求和、与门限比较、再进行非线性运算，得到网络的输出-The network will be weighted sum input mode, compared with the threshold, then a nonlinear operator to get the output of the network Platform: |
Size: 493568 |
Author:徐森 |
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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: 通过搭建多层输入与输出的神经网络，通过学习，对输出进行预测，从而实现对不同参数的评估-Through constructing multiple input and output of neural network, through the study, forecast the output, so as to realize the different parameters of the uation Platform: |
Size: 7987200 |
Author:baizhongxing |
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Search - output of neural network