Description: 从ML-EM 重建算法入手，分析了贝叶斯模型的一些关键点，针对采用传统方法求解MAP问题的局限性，提出一种用于正电子成像的贝叶斯神经网络重建算法，为了保留边缘信息，引入了二进制的保边缘变量，并应用共轭神经网络求解，模拟的重建结果表明，应用这种算法可以得到比ML-EM 算法更好的重建图像@-From the ML-EM reconstruction algorithm start with an analysis of Bayesian model some of the key points, the use of traditional methods for solving the problem of the limitations of MAP, a positron imaging for the Bayesian neural network algorithm, in order to to retain the edge information, the introduction of binary variables edge security, and application of neural network for solving the conjugate, the reconstruction of simulated results show that the algorithm can be more than ML-EM image reconstruction algorithm better @ Platform: |
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Author:李浩 |
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Description: 本文探讨了神经网络用于图像压缩的方法，提出了一种基于边缘信息检测的BP神经网络压缩方法:按图像各部分像素特征将图像分为平滑块、纹理块和边缘块，不同的分类块采用不同的隐含层数。实验结果证明本算法在在保证重建图像丰富细节的同时，能大大提高图像的压缩比。-This paper discusses the neural network for image compression method, edge information is presented based on BP neural network detection of compression method: According to the various parts of the image pixels will be characterized by a smooth image is divided into blocks, the texture blocks and edge blocks, different types of blocks implied different layers. Experimental results show that the algorithm in detail in ensuring the reconstruction of the image-rich at the same time, can greatly improve the image compression ratio. Platform: |
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Author:王蒙 |
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Description: Multi-step-prediction of chaotic time series based
on co-evolutionary recurrent neural network
协同进化递归神经网络的多步混沌时间序列预测-This paper proposes a co-evolutionary recurrent neural network (CERNN) for the multi-step-prediction of chaotic
time series, it estimates the proper parameters of phase space reconstruction and optimizes the structure of recurrent
neural networks by co-evolutionary strategy. The searching space was separated into two subspaces and the individuals
are trained in a parallel computational procedure. It can dynamically combine the embedding method with the capability
of recurrent neural network to incorporate past experience due to internal recurrence. The eff ectiveness of CERNN is
evaluated by using three benchmark chaotic time series data sets: the Lorenz series, Mackey–Glass series and real-world
sun spot series. The simulation results show that CERNN improves the performances of multi-step-prediction of chaotic
time series. Platform: |
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Author: |
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Description: 利用matlab自带的BP神经网络工具箱对duffing混沌背景模型进行相空间重构与预测，程序中已经给出duffing函数，主函数中也给出了主要注释，方便大家学习交流。-BP' s own use matlab neural network toolbox for duffing chaotic background model and prediction of phase space reconstruction, the program has been given duffing function, the main function also gives the main note, we facilitate learning exchanges. Platform: |
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Author: |
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Description: 基于时间序列相空间重构和RBF神经网络的短时交通流量预测学习方法-Based on time series phase space reconstruction and RBF neural network method of short-term traffic flow prediction study Platform: |
Size: 6030336 |
Author:王强 |
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Description: 提出改进型的节点重构小波包频带能量谱与 PNN（概率神经网络）的联合故障诊断新方法。 有具体的数据和程序，经过本人验证的，程序有效！效果很好！-Joint fault diagnosis node propose a new method for the reconstruction of improved wavelet packet energy spectrum band and PNN (probabilistic neural network). Specific data and programs, after I verified, the program works! Works well! Platform: |
Size: 575488 |
Author:李其龙 |
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Description: 神经网络算法和小波分解与重构，BP（Back Propagation）网络，是一种按误差逆传播算法训练的多层前馈网络-Neural network algorithm and wavelet decomposition and reconstruction, BP (Propagation Back) network, is a kind of multilayer feedforward network trained by the error back propagation algorithm. Platform: |
Size: 3072 |
Author:wangjunke |
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Description: 通过卷积神经网络CNN实现超分辨率重建，利用训练模型实现参数和权重偏移的训练，达到输入低分辨率图像，输出高分辨率图像，测试例程附录。-Convolution neural network CNN to achieve super-resolution reconstruction using a training model parameters and to achieve weight shift training to achieve a low-resolution image input and output high-resolution image, test routine appendix. Platform: |
Size: 3072 |
Author:ll |
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Description: 通过神经网络实现高维无线通信信道压缩反馈与重建、低维信道的数据高效传输问题。
nmse小于0.08(The neural network is used to realize the compression feedback and reconstruction of high-dimensional wireless communication channel and the efficient data transmission of low-dimensional channel.
NMSE is less than 0.08) Platform: |
Size: 4096 |
Author:和卓然666 |
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Search - neural network and reconstruction