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: 一个神经网络计算的库，实现几个通用神经网络体系和训练方法，自识别图，弹性网络等.like Back Propagation, Kohonen Self-Organizing Map, Elastic Network, Delta Rule Learning, and Perceptron Learning.-In this article, a C# library for neural network computations is described. The library implements several popular neural network architectures and their training algorithms, like Back Propagation, Kohonen Self-Organizing Map, Elastic Network, Delta Rule Learning, and Perceptron Learning. The usage of the library is demonstrated on several samples:
• Classification (one-layer neural network trained with perceptron learning algorithms)
• Approximation (multi-layer neural network trained with back propagation learning algorithm)
• Time Series Prediction (multi-layer neural network trained with back propagation learning algorithm)
• Color Clusterization (Kohonen Self-Organizing Map)
• Traveling Salesman Problem (Elastic Network). Platform: |
Size: 242688 |
Author:calford |
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Description: 对量子粒子群优化(QPSO) 算法进行研究，提出了自适应量子粒子群优化(Adaptive QPSO) 算法，用于优化Elman 神经
网络的参数，改进了Elman 神经网络的泛化能力。利用网络流量时间序列数据进行预测，实验结果表明，采用AQPSO 算法优
化获得的Elman 神经网络模型不但具有较强的泛化能力，而且具有良好的稳定性，在网络流量时间序列数据的预测中具有
一定的实用价值-Quantum-behaved particle swarm optimization (QPSO) algorithm is researched and adaptive quantum-behaved particle
swarm optimization (AQPSO) algorithm is proposed in order to improve network’s performance. By applying AQPSO algorithm to
train the net parameters adopted in the Elman neural network, the generalization ability of the Elman neural network is improved. Experimental
results with network traffic time series data forecasting sets show that obtained network model has not only good generalization
properties, but also has better stability. It illustrates that Elman net with AQPSO optimization algorithm has the promising application
in network traffic time series data prediction. Platform: |
Size: 312320 |
Author:张杰 |
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Description: 该程序为一个用神经网络预测时间序列，界面不错，经运行效果也很好。-This program for a time series prediction with neural network, the interface is good, the operation effect is very good also.
Platform: |
Size: 1024 |
Author:jiangnan |
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Description: 采用模糊神经网络anfis预测混沌时间序列的源程序。-The source program of Using fuzzy ANFIS neural network for predicting chaotic time series. Platform: |
Size: 571392 |
Author:胡玉霞 |
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Description: ＢＰ神经网络具有较强的非线性问题处理能力 是目前一 种 较 好 的 用 于 时 间 序 列 预 测 的 方 法 然 而 它 存 在 易 于 陷 入 局 部 极 小,针对地震预测的应用,用改进粒子群优化的ＢＰ算法对四川地区最大震级时间序列进行预测,通过训练 预 测 次 年 的 最 大 震 级 结 果,表明此方法优于未经优化的 ＢＰ算法具有良好的预测效果 -BP neural network has a strong nonlinear problems processing power is a method for time series prediction, however it is easy to fall into local minimum, the application for earthquake prediction, BP algorithm with improved particle swarm optimization Sichuan maximum magnitude time series prediction by training the forecast for the following year the results of the maximum magnitude, indicating that this method is better than the non-optimized BP algorithm has good predictive Platform: |
Size: 214016 |
Author:mali |
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Description: 支持向量机回归理论与神经网络等非线性回归理论相比具有许多独特的优点有线性回归和非线性回归,其模型的选 择包括核的选择、容量控制以及损失函数的选择.在控制方面的研究包括非线性 时间序列 的预测及应用、系统辨识以及优化控制和学习控制等方面的研究-Support vector machine (SVM) regression theory and neural network has many unique advantages such as nonlinear regression theory with linear regression and nonlinear regression, the choice of its model including the selection of nuclear, volume control, and the choice of loss function. In the control of nonlinear time series prediction and application, including system identification and optimization control and learning control research Platform: |
Size: 459776 |
Author:mumu |
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Description: Mackey-Glass时间序列预测建模问题，建立了四输入一输出、具有3层结构的MLP人工神经网络，实现了函数逼近的功能。-Mackey-Glass time series prediction modeling, the establishment of a four-input one output, with a 3-layer MLP artificial neural network architecture to achieve a function approximation functions. Platform: |
Size: 1024 |
Author:乔木 |
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Search - time series prediction with neural network