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Description: 深度学习（Deep learning）matlab工具包-`NN/` - A library for Feedforward Backpropagation Neural Networks
`CNN/` - A library for Convolutional Neural Networks
`DBN/` - A library for Deep Belief Networks
`SAE/` - A library for Stacked Auto-Encoders
`CAE/`- A library for Convolutional Auto-Encoders
`util/`- Utility functions used by the libraries
`data/`- Data used by the examples
`tests/`- unit tests to verify toolbox is working
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Author: Xiaojuan Jiang |
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Description: voice recognition with neural networks backpropagation. this source can recognize number one two three four five six seven eight nine
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Size: 28157952 |
Author: melly |
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Description: It can be used for backpropagation, perceptron
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Size: 279552 |
Author: Adeel |
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Description: Sintak Program Backpropagation
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Size: 64512 |
Author: Naff |
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Description: Sintak Program Backpropagation
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Size: 1024 |
Author: Naff |
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Description: This the error backpropagation algorithm based character recognition program developed in matlab. The program folder contains the training images of some english alphabets & numerals. & trains the neural network using train command in matlab. The neural network is initialized using newff command.
After the simulation, the program recognizes the input character. Accuracy on the provided images is 100 .-This is the error backpropagation algorithm based character recognition program developed in matlab. The program folder contains the training images of some english alphabets & numerals. & trains the neural network using train command in matlab. The neural network is initialized using newff command.
After the simulation, the program recognizes the input character. Accuracy on the provided images is 100 .
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Author: manish |
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Description: backpropagation in vb language
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Size: 16384 |
Author: rabiaa |
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Description: It is demonstrated that neural networks can be used effectively for the identification and control of nonlinear dynamical systems. The emphasis on models for both identification and control. Static and dynamic backpropagation methods for the adjustment of parameters are discussed. In the models that are introduced, multilayer and recurrent networks are interconnected in novel configurations, and hence there is a real need to study them in a unified fashion. Simulation results reveal that the identification and adaptive control schemes suggested are practically feasible. Basic concepts and definitions are introduced throughout, and theoretical questions that have to be addressed are also described-It is demonstrated that neural networks can be used effectively for the identification and control of nonlinear dynamical systems. The emphasis is on models for both identification and control. Static and dynamic backpropagation methods for the adjustment of parameters are discussed. In the models that are introduced, multilayer and recurrent networks are interconnected in novel configurations, and hence there is a real need to study them in a unified fashion. Simulation results reveal that the identification and adaptive control schemes suggested are practically feasible. Basic concepts and definitions are introduced throughout, and theoretical questions that have to be addressed are also described
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Size: 3072 |
Author: mike419 |
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Description: ANN on back propagation
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Size: 136192 |
Author: Dipjyoti Metia |
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Description: OFDM系统中的信道估计和均衡使用反向传播神经网络。-Channel estimation and equalization using backpropagation neural networks in OFDM systems.
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Size: 637952 |
Author: Johnson |
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Description: 这是一个四个不同的S函数实现集合的递归模糊神经网络（RFNN）。该网络采用了4组可调参数,这使得它非常适合在线学习/操作，从而可应用到系统识别等方面。-This is a collection of four different S-function implementations of the recurrent fuzzy neural network (RFNN) described in detail in [1]. It is a four-layer, neuro-fuzzy network trained exclusively by error backpropagation at layers 2 and 4. The network employs 4 sets of adjustable parameters. In Layer 2: mean[i,j], sigma[i,j] and Theta[i,j] and in Layer 4: Weights w4[m,j]. The network uses considerably less adjustable parameters than ANFIS/CANFIS and therefore, its training is generally faster. This makes it ideal for on-line learning/operation. Also, its approximating/mapping power is increased due to the employment of dynamic elements within Layer 2. Scatter-type and Grid-type methods are selected for input space partitioning.
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Size: 117760 |
Author: 林真天 |
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Description: connection file for backpropagation
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Size: 1024 |
Author: Setiawan |
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Description: BP神经网络训练程序，基于梯度学习算法，对非线性函数（hermitian）逼近。-The BP neural network training program，which is based on the steepest descent Method，，，backpropagation network (BP) approximation of the nonlinear function。
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Size: 1024 |
Author: 吕钱 |
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Description: BackPropagation, implemented by matlab, for those who are new in neural networks
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Size: 2332672 |
Author: zahra |
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Description: it s backpropagation neural network for iris data set
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Size: 1024 |
Author: bahar |
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Description: 识别手写数字的人工神经网络的发展神经网络概念，如神经元，层，重量和反向传播等概念及c++实现在文中给出-This article chronicles the development of an artificial neural network designed to recognize handwritten digits. Although some theory of neural networks is given here, it would be better if you already understood some neural network concepts, like neurons, layers, weights, and backpropagation.
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Size: 317440 |
Author: 忠于理想 |
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Description: This a sample of a simple image classification using K-Nearest Neighbor and Backpropagation Neural Network. It uses block averaging in feature extraction process.-This is a sample of a simple image classification using K-Nearest Neighbor and Backpropagation Neural Network. It uses block averaging in feature extraction process.
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Size: 175104 |
Author: mahmuddwis |
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Description: 倒傳遞類神經網路,廣泛應用於人工智慧系統-Neural backpropagation is the phenomenon in which the action potential of a neuron creates a voltage spike both at the end of the axon (normal propagation) and back through to the dendritic arbor or dendrites, from which much of the original input current originated. It has been shown that this simple process can be used in a manner similar to the backpropagation algorithm used in multilayer perceptrons, a type of artificial neural network. In addition to active backpropagation of the action potential, there is also passive electrotonic spread.
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Size: 3072 |
Author: stanley |
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Description: algorithm of backpropagation demo
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Size: 244736 |
Author: prasetyo |
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Description: The esiest way to learn the backpropagation algorithm and theory.
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Size: 138240 |
Author: f33744_k |
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