Experimental results indicate that the fast BP algorithm in MATLAB toolbox has very high practical value.
实验结果表明，MATLAB工具箱中的快速BP算法具有很高的实用价值。
The networks are trained by the fast BP algorithm via fuzzy variables decision, and training samples are provided by the dynamic inversion control results.
网络的训练利用改进的BP算法，将因子模糊化快速进行。样本点数据则由利用动态逆控制所得到的结果来提供。
A scale training algorithm of BP neural network is used, and sample reorganization method is proposed. Its advantage is the fast training speed and good feature extraction ability.
作者使用比例训练的BP算法，提出对训练模式进行样本重组的方法，其特点是训练速度快、特征抽取能力强。
According to the analysis of simulation results compared with BP algorithm, this algorithm has the advantage of the fine stability, fast convergence speed and high precision.
通过与BP算法的仿真结果比较分析，发现该算法具有稳定性好，收敛速度快，预测精度高的特点。
In order to reduce the operation cost and optimize the unit commitment, the fast algorithm about unit commitment based on revised BP ANN (Artificial Neural Network) and dynamic search is discussed.
为了使机组达到最优组合，减少运行成本，研究了基于修正BP人工神经网络与动态搜索的快速算法在机组组合中的运用。
Based on adaptive LMS algorithms, the on line BP algorithm with fast convergence speed is presented.
在自适应LMS算法基础上，提出了在线BP训练算法、收敛速度快。
The new algorithm, compared to the BP algorithm, has the fast learning rate and good convergence properties.
该算法有效地改进了神经元网络的学习收敛速度，取得了比常规BP算法更好的收敛性能和学习速度。
Through the analysis of simulation results as compared with BP algorithm, this algorithm has the advantages of fine stability, fast convergence speed and high precision.
通过与BP算法的仿真结果比较分析，发现该算法稳定性好，收敛速度快，预测精度高。
To accelerate the training speed of BP network, a joint-optimized fast BP learning algorithm is proposed.
针对BP网络学习速度的缓慢性，本文提出了一种联合优化后的快速学习算法。
The algorithm is applied to XOR problem and nonlinear function approximation. Simulation results show that the chaos-BP algorithm needs shorter learning time than that of the standard BP and fast BP.
采用混合算法对XOR问题和非线性函数进行仿真，结果表明该算法明显优于标准BP算法和快速BP算法。
The algorithm is applied to XOR problem and nonlinear function approximation. Simulation results show that the chaos-BP algorithm needs shorter learning time than that of the standard BP and fast BP.
采用混合算法对XOR问题和非线性函数进行仿真，结果表明该算法明显优于标准BP算法和快速BP算法。
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