Description: 自适应(Adaptive)神经网络源程序
The adaptive Neural Network Library is a collection of blocks that implement several Adaptive Neural Networks featuring
different adaptation algorithms.~..~
There are 11 blocks that implement basically these 5 kinds of neural networks:
1) Adaptive Linear Network (ADALINE)
2) Multilayer Layer Perceptron with Extended Backpropagation algorithm (EBPA)
3) Radial Basis Functions (RBF) Networks
4) RBF Networks with Extended Minimal Resource Allocating algorithm (EMRAN)
5) RBF and Piecewise Linear Networks with Dynamic Cell Structure (DCS) algorithm
A simulink example regarding the approximation of a scalar nonlinear function of 4 variables -Adaptive (Adaptive) The neural network source adaptive Neural Network Library is a collection of blocks that implement several Adaptive Neural Networks featuring different adaptation algorithms .~..~ There are 11 blocks that implement basically these five kinds of neural networks : a) Adaptive Linear Network (ADALINE) 2) 102206 with Multilayer Layer Extended Backpropagation algorithm (EBPA) 3) Radial Basis Functions (RBF) Networks, 4) RBF Networks with Extended Minimal Resource Allocating algorithm (EMRAN) 5) RBF and Piecewise Linear Dynamic Networks with the Cell Structure (DCS) algorithm A Simulink example regarding the approximation of a scalar nonlinear function of four variables Platform: |
Size: 200530 |
Author:周志连 |
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Description: The adaptive Neural Network Library is a collection of blocks that implement several Adaptive Neural Networks featuring
different adaptation algorithms.~..~
There are 11 blocks that implement basically these 5 kinds of neural networks:
1) Adaptive Linear Network (ADALINE)
2) Multilayer Layer Perceptron with Extended Backpropagation algorithm (EBPA)
3) Radial Basis Functions (RBF) Networks
4) RBF Networks with Extended Minimal Resource Allocating algorithm (EMRAN)
5) RBF and Piecewise Linear Networks with Dynamic Cell Structure (DCS) algorithm
A simulink example regarding the approximation of a scalar nonlinear function of 4 variables is included-The adaptive Neural Network Library is a collection of blocks that implement several Adaptive Neural Networks featuring different adaptation algorithms .~..~ There are 11 blocks that implement basically these five kinds of neural networks : a) Adaptive Linear Network (ADALINE) 2) Multilayer Layer 102206 with Extended Backpropagation algorithm (EBPA) 3) Radial Basis Functions (RBF) Networks, 4) RBF Networks with Extended Minimal Resource Allocating algorithm (EMRAN) 5) and RBF Networks with Piecewise Linear Dynamic Cell Structure (DCS) algorithm A Simulink example regarding the approximation of a scalar nonlinear function of four variables is included Platform: |
Size: 198792 |
Author:叶建槐 |
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Description: 这是我参考了n(n>=5,^_^)篇BP神经网优化方法的论文写出的BP神经网源代码，使用了批处理训练方法，带动量项、学习速率的自适应调整、仅对学习精度没有达到指定要求的模式训练，并且训练精度逐步加大，通过这种方法，极大地加快了训练速度；另外，对于0模式和1模式数目相差很大(5-6倍)都能达到很高的学习精度。-This is my reference to the n (NGT; Chapter 5 = ,^_^) BP neural network optimization method to write the thesis of BP neural network source code, use the batch method of training, the volume driven, adaptive learning rate adjustment, not only learning accuracy requirements specified the model training, and training gradually increased accuracy, this method has greatly accelerated the pace of training; In addition, the 0 mode and a model number vary greatly (5-6 times) to achieve high accuracy of the study. Platform: |
Size: 4096 |
Author:王宏志 |
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Description: 落煤残存瓦斯量的确定是采掘工作面瓦斯涌出量预测的重要环节,它直接影响着采掘工作面瓦斯涌出量预测的精度,并与煤的变质程度、落煤粒度、原始瓦斯含量、暴露时间等影响因素呈非线性关系。人工神经网络具有表示任意非线性关系和学习的能力,是解决复杂非线性、不确定性和时变性问题的新思想和新方法。基于此,作者提出自适应神经网络的落煤残存瓦斯量预测模型,并结合不同矿井落煤残存瓦斯量的实际测定结果进行验证研究。结果表明,自适应调整权值的变步长BP神经网络模型预测精度高,收敛速度快 该预测模型的应用可为采掘工作面瓦斯涌出量的动态预测提供可靠的基础数据,为采掘工作面落煤残存瓦斯量的确定提出了一种全新的方法和思路。-charged residual coal gas is to determine the volume of mining gas emission rate forecast an important link, which directly affect mining gas emission rate forecast accuracy, and with coal metamorphism, loading coal particle size, the original gas content, exposure time and other factors nonlinear relationship. Artificial neural networks have expressed arbitrary nonlinear relationships and the ability to solve complex nonlinear, time-varying uncertainty and the new ideas and new approaches. Based on this, the author of adaptive neural network loading coal residual gas production forecast model, and a combination of different loading coal mine gas remnants of the actual test results of research. Results show that the adaptive value of the right to change step BP neural network model predict Platform: |
Size: 60416 |
Author:王静 |
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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: 自适应(Adaptive)神经网络源程序
The adaptive Neural Network Library is a collection of blocks that implement several Adaptive Neural Networks featuring
different adaptation algorithms.~..~
There are 11 blocks that implement basically these 5 kinds of neural networks:
1) Adaptive Linear Network (ADALINE)
2) Multilayer Layer Perceptron with Extended Backpropagation algorithm (EBPA)
3) Radial Basis Functions (RBF) Networks
4) RBF Networks with Extended Minimal Resource Allocating algorithm (EMRAN)
5) RBF and Piecewise Linear Networks with Dynamic Cell Structure (DCS) algorithm
A simulink example regarding the approximation of a scalar nonlinear function of 4 variables -Adaptive (Adaptive) The neural network source adaptive Neural Network Library is a collection of blocks that implement several Adaptive Neural Networks featuring different adaptation algorithms .~..~ There are 11 blocks that implement basically these five kinds of neural networks : a) Adaptive Linear Network (ADALINE) 2) 102206 with Multilayer Layer Extended Backpropagation algorithm (EBPA) 3) Radial Basis Functions (RBF) Networks, 4) RBF Networks with Extended Minimal Resource Allocating algorithm (EMRAN) 5) RBF and Piecewise Linear Dynamic Networks with the Cell Structure (DCS) algorithm A Simulink example regarding the approximation of a scalar nonlinear function of four variables Platform: |
Size: 200704 |
Author:周志连 |
Hits:
Description: The adaptive Neural Network Library is a collection of blocks that implement several Adaptive Neural Networks featuring
different adaptation algorithms.~..~
There are 11 blocks that implement basically these 5 kinds of neural networks:
1) Adaptive Linear Network (ADALINE)
2) Multilayer Layer Perceptron with Extended Backpropagation algorithm (EBPA)
3) Radial Basis Functions (RBF) Networks
4) RBF Networks with Extended Minimal Resource Allocating algorithm (EMRAN)
5) RBF and Piecewise Linear Networks with Dynamic Cell Structure (DCS) algorithm
A simulink example regarding the approximation of a scalar nonlinear function of 4 variables is included-The adaptive Neural Network Library is a collection of blocks that implement several Adaptive Neural Networks featuring different adaptation algorithms .~..~ There are 11 blocks that implement basically these five kinds of neural networks : a) Adaptive Linear Network (ADALINE) 2) Multilayer Layer 102206 with Extended Backpropagation algorithm (EBPA) 3) Radial Basis Functions (RBF) Networks, 4) RBF Networks with Extended Minimal Resource Allocating algorithm (EMRAN) 5) and RBF Networks with Piecewise Linear Dynamic Cell Structure (DCS) algorithm A Simulink example regarding the approximation of a scalar nonlinear function of four variables is included Platform: |
Size: 198656 |
Author:叶建槐 |
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Description: 拿verilog编写的som(自适应神经网络算法)，用于障碍物检测，基于FPGA可综合实验，已经在altera的cylcone上实现-Canal verilog prepared som (adaptive neural network algorithm) for obstacle detection. Based on FPGA synthesis experiments, in altera achieve the cylcone Platform: |
Size: 5120 |
Author:刘索山 |
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Description: 神经网络方面自适应滤波算法的综述，希望大家有用-neural network algorithm for the adaptive filter on the hope that it may be useful Platform: |
Size: 94208 |
Author:卢宇豪 |
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Description: The adaptive Neural Network Library is a collection of blocks that implement several Adaptive Neural Networks featuring different adaptation algorithms.
Platform: |
Size: 210944 |
Author:李立 |
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Description: 近几年来研究的多种PID控制程序，有灰色PID，神经网络自适应PID，模糊PID等等，！
-Research in recent years a variety of PID control procedures, has gray PID, Neural Network Adaptive PID, fuzzy PID, etc.! Platform: |
Size: 5120 |
Author:wjy |
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Description: 讨论了BP 小波神经网络在训练过程中减小误差函数时最优方向的确定和自适应调整学习率的方法。
首先论证了小波神经网络的数学基础,然后讨论了BP 小波神经网络的学习过程,重点讨论了减小误差函数最优方
向的确定方法,即如何保证步长方向与负梯度方向一致,由此得出了自适应调整学习率的简便方法。该方法具有
普遍性,有广泛的应用价值。仿真结果表明,采用最优梯度下降方向可以大幅度提高BP 小波神经网络的学习速
度。-Discussed BP wavelet neural network in the training process of reducing the error function to determine the optimal direction and adaptive learning rate adjustment method. First of all, demonstrated that the wavelet neural network mathematical basis, and then discussed the BP wavelet neural network learning process focused on reducing the error function to determine the optimal direction of approach, namely how to ensure that the direction of step with the negative gradient direction, which may out the adaptive learning rate adjustment easy way. The method is universal, has a wide range of application value. Simulation results show that the optimal direction of gradient descent can dramatically improve BP wavelet neural network learning speed. Platform: |
Size: 245760 |
Author:guole |
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Description: 提出了一种基于小波神经网络整定的PID 控制方法。由于小波变换具有良
好的时频局部特性,神经网络具有强大的非线性映射能力,自学习、自适应等优势,采用规
范正交的小波函数作为神经网络的基函数构成小波神经网络,该网络兼有小波函数的紧
支性、波动性以及神经网络的非线性映射能力,自学习、自适应能力等优点,渗碳炉控制实
验结果表明,用该方法整定的PID 控制系统收敛速度快,逼近精度高,鲁棒性好-Based on wavelet neural network-tuning of PID control methods. Since the wavelet transform has good time-frequency localization properties, neural network has strong ability of nonlinear mapping, self-learning, adaptive and other advantages, the use of standardized orthogonal wavelet function as a neural network constitutes a wavelet basis function neural network, the network a combination of compactly supported wavelet function, and volatility as well as the neural network nonlinear mapping ability, self-learning, adaptive capacity, etc., carburizing furnace control experimental results show that using this method of tuning PID control system for fast convergence approximation of high accuracy, good robustness Platform: |
Size: 192512 |
Author:guole |
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Description: ，自适应神经网络噪声抵消系统不需要关于输入信号的先验知识，非线性映射能力强，具有自学习能力、计算量小和实时性好等特点，利用该系统对含噪声的非线性信号建模，可达到消除噪声的目的。-The adaptive neural network noise canceling system does not need prior knowledge about the input signal, strong nonlinear mapping ability, self-learning ability, low computation and good real-time performance. Mode, can achieve the purpose of eliminating noise. Platform: |
Size: 330752 |
Author:彼岸花 |
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