Description: BP（Back Propagation）网络是1986年由Rumelhart和McCelland为首的科学家小组提出，是一种按误差逆传播算法训练的多层前馈网络，是目前应用最广泛的神经网络模型之一。BP网络能学习和存贮大量的输入-输出模式映射关系，而无需事前揭示描述这种映射关系的数学方程。它的学习规则是使用最速下降法，通过反向传播来不断调整网络的权值和阈值，使网络的误差平方和最小。BP神经网络模型拓扑结构包括输入层（input）、隐层(hide layer)和输出层(output layer)。-BP (Back Propagation) network in 1986 by Rumelhart and McCelland led team of scientists proposed an algorithm by error back propagation trained multilayer feedforward network, is currently the most widely used one neural network model. BP network can learn and store a lot of input- output model mapping, without prior mapping reveals the mathematical description of this equation. Its learning rule is to use the steepest descent method, by back-propagation network to continuously adjust the weights and thresholds, so the network and the minimum sum of squared errors. BP neural network topology, including the input layer (input), hidden layer (hide layer) and output layer (output layer). Platform: |
Size: 2048 |
Author:陈财雄 |
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Description: BP（Back Propagation）网络是1986年由Rumelhart和McCelland为首的科学家小组提出，是一种按误差逆传播算法训练的多层前馈网络，是目前应用最广泛的神经网络模型之一。BP网络能学习和存贮大量的输入-输出模式映射关系，而无需事前揭示描述这种映射关系的数学方程。它的学习规则是使用最速下降法，通过反向传播来不断调整网络的权值和阈值，使网络的误差平方和最小。BP神经网络模型拓扑结构包括输入层（input）、隐层(hide layer)和输出层(output layer)。-BP (Back Propagation) network in 1986 by Rumelhart and McCelland led team of scientists proposed an algorithm by error back propagation trained multilayer feedforward network, is currently the most widely used one neural network model. BP network can learn and store a lot of input- output model mapping, without prior mapping reveals the mathematical description of this equation. Its learning rule is to use the steepest descent method, by back-propagation network to continuously adjust the weights and thresholds, so the network and the minimum sum of squared errors. BP neural network topology, including the input layer (input), hidden layer (hide layer) and output layer (output layer). Platform: |
Size: 1024 |
Author:陈财雄 |
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Description: BP（Back Propagation）网络是1986年由Rumelhart和McCelland为首的科学家小组提出，是一种按误差逆传播算法训练的多层前馈网络，是目前应用最广泛的神经网络模型之一。BP网络能学习和存贮大量的输入-输出模式映射关系，而无需事前揭示描述这种映射关系的数学方程。它的学习规则是使用最速下降法，通过反向传播来不断调整网络的权值和阈值，使网络的误差平方和最小。BP神经网络模型拓扑结构包括输入层（input）、隐层(hide layer)和输出层(output layer)。-BP (Back Propagation) network in 1986 by Rumelhart and McCelland led team of scientists proposed an algorithm by error back propagation trained multilayer feedforward network, is currently the most widely used one neural network model. BP network can learn and store a lot of input- output model mapping, without prior mapping reveals the mathematical description of this equation. Its learning rule is to use the steepest descent method, by back-propagation network to continuously adjust the weights and thresholds, so the network and the minimum sum of squared errors. BP neural network topology, including the input layer (input), hidden layer (hide layer) and output layer (output layer). Platform: |
Size: 2048 |
Author:陈财雄 |
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Description: 用一个多层前向神经网络，采用反向传播算法依据控制要求实时输出Kp、Ki、Kd，依次作为PID控制器的实时参数，代替传统PID参数靠经验的人工整定和工程整定，以达到对大迟延主气温系统的良好控制。-We use a multilayer feedforward neural network using back propagation algorithm and based on control requirements.This net can real-time output Kp, Ki, Kd as the PID controller parameters ,insteading of the traditional PID parameters determined by experience. So it can obtain good control performance . Platform: |
Size: 577536 |
Author:durongmao |
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Description: 该BP算法，采用网络上盛行的，以模拟电路实例，检测该算法的可行性-The BP algorithm, using popular network to simulate the circuit instance, testing the feasibility of the algorithm Platform: |
Size: 3072 |
Author:邓智一 |
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Description: Isolated speech recognition using FFT for features extraction and Artificial Neural Network with Back Propagation for classificassion and recognition. Platform: |
Size: 149504 |
Author:Utis Sutisna |
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Description: 此程式是類神經演算法之中倒傳遞網路程式碼
此mse結果並沒有完全達到標準-This program is among the back-propagation neural network algorithm code for this mse results are not fully up to standard Platform: |
Size: 20480 |
Author:Wu |
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Description: 构建BP神经网络，源码。BP网络能学习和存贮大量的输入-输出模式映射关系，而无需事前揭示描述这种映射关系的数学方程。它的学习规则是使用最速下降法，通过反向传播来不断调整网络的权值和阈值，使网络的误差平方和最小。BP神经网络模型拓扑结构包括输入层（input）、隐层(hide layer)和输出层(output layer)。-BP neural network to build, source. BP network can learn and store a lot of input- output model mapping, without having to reveal in advance the mathematical description of the mapping equation. Its learning rule is to use the steepest descent method, by back-propagation network to continuously adjust the weights and threshold, the network and the minimum square error. BP neural network model topology including input layer (input), hidden (hide layer) and output layer (output layer). Platform: |
Size: 4096 |
Author:chenqian |
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Description: 神经网络原理 Simon Haykin
基本涵盖了神经网络的许多基础部分和重要方面。像Back Propagation， Radial-Basis Function，Self-Organizing Maps，以及single neuron中的Hebbian Learning, Competitive Learning和LMS Learning。 -Neural network theory Simon Haykin covering the basic part of the neural network and the many important aspects. Like Back Propagation, Radial-Basis Function, Self-Organizing Maps, and the single neuron in the Hebbian Learning, Competitive Learning and LMS Learning. Platform: |
Size: 18356224 |
Author: |
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