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: 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: 60227 |
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: 34625 |
Author:张洁 |
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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 |
Hits:
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 |
Hits:
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: 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: 本文主要研究利用神经网络进行非线性辨识及自适应控制。
-This paper studied the feasibility of using neural network nonlinear identification and adaptive control. Platform: |
Size: 1412096 |
Author:陆见 |
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Description: Abstract—Stable direct and indirect decentralized adaptive radial basis
neural network controllers are presented for a class of interconnected
nonlinear systems. The feedback and adaptation mechanisms for each
subsystem depend only upon local measurements to provide asymptotic
tracking of a reference trajectory. Due to the functional approximation
capabilities of radial basis neural networks, the dynamics for each
subsystem are not required to be linear in a set of unknown coeffi cients
as is typically required in decentralized adaptive schemes. In addition,
each subsystem is able to adaptively compensate for disturbances and
interconnections with unknown bounds. Platform: |
Size: 151552 |
Author:centema |
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Description: 神经网络是由大量简单的基本元件—神经元相互连接而成的自适应非线性动态系统。每个神经元的结构和功能比较简单，而大量神经元组合产生的系统行为却非常复杂。-Neural network is a large number of simple basic components- made of neurons interconnected adaptive nonlinear dynamic systems. Each neuron structure and function of relatively simple, while the combination of a large number of neurons resulting system behavior is very complex. Platform: |
Size: 587776 |
Author:hkm |
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Description: 由于人工神经网络具有大规模并行信息处理，良好的自适应与自学习
等许多特点，因此利用神经网络解决复杂非线性动态系统的预测问题就有了一条新的可能途径
-As massively parallel artificial neural network information processing, a good adaptive and self-learning and many other features, the use of neural networks to solve complex nonlinear dynamic system of prediction may have a new way Platform: |
Size: 3972096 |
Author:杨利明 |
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Description: 基于神经网络在线辨识的自适应逆振动控制技术。可以有效地应用到非线性系统的控制。-Line identification based on neural network adaptive inverse vibration control technology. Can be effectively applied to nonlinear system control. Platform: |
Size: 14336 |
Author:罗波 |
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Description: ：针对能够采用仿射非线性表示的含有未建模动态的SISO非线性系统，讨论了一种基于神经网络的自适应
控制方法．该方法对受控对象的已知部分．采用反馈线性化方法设计控制器，用神经网络在线补偿未建模动态及
外部干扰等引起的误差，从而实现自适应控制。对具有未建模动态的双车倒立摆设计了输出反馈自适应控制系
统．仿真表明该方法是有效的。 -A discussion is devoted to design neural network adaptive control scheme of the SISO (single
input and single output)nonlinear system with unmodeled dynamics．According to the known part of
the plant．feedback Iinearization method iS used to design the controller．The error resulted from the un～
modeled dynamics and the external disturbance is compensated by online neural network．The neural
networks are designed as a five layer fuzzy neural network and its construction is optimized by genetic al—
gorithms．It has been used to approtimate the nonlinear function of system and to compesate the error of
unmodeled dynamic．The design of neural network adaptive controller has better performances．The
method is verified by the digital simulation of tWO—·cart with inverted·-pendulum system and unmodeled
dynamics． Platform: |
Size: 163840 |
Author: |
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Description: BP神经网络，包括由大量的简单基本元件——神经元相互联接而成的自适应非线性动态系统。每个神经元的结构和功能比较简单，但大量神经元组合产生的系统行为却非常复杂。-BP neural network，By a large number of simple basic components- neurons interconnected by adaptive nonlinear dynamic systems. The structure and function of each neuron is relatively simple, but a large number of neurons in combination to produce the system behavior is very complex. Platform: |
Size: 5120 |
Author:曾军 |
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Description: this is a maltab/sumulink simulation of &a novel adaptive neural control of nonlinear systems with actuator saturation,it simulates the algorithm proposed by slemic in 2004. Platform: |
Size: 8192 |
Author:boulou |
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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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Description: 1.elman神经网络对输入波形进行检测
2.设计具有3个神经元的Hopfield网络
3.建立自适应神经模糊推理系统对非线性函数进行逼近（正弦加滞后）
4.建立自适应神经模糊推理系统对非线性函数进行逼近（正弦多项式）
5.利用模糊C均值聚类方法将一类随机给定的三维数据分为三类(1.Detection of input waveform by elman neural network
2. design a Hopfield network with 3 neurons
3. establish adaptive neuro fuzzy inference system to approximate nonlinear functions (sine plus lag).
4. establish adaptive neuro fuzzy inference system to approximate nonlinear functions (sine polynomials).
5. fuzzy C means clustering method is used to divide a class of randomly given 3D data into three categories.) Platform: |
Size: 2048 |
Author:南风水忆 |
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Search - adaptive nonlinear neural network