本文提出基于新的激励函数BP算法建立误差预测模型，修正新型广义预测算法的预测输出。
In the paper presents the predictive out of a new generalized predictive Control is corrected by the error predictive model based on a new excite function BP arithmetic.
文章在综合利用模糊模式识别剔除噪音信息和BP神经网络拟合优势的基础上，设计了模糊神经网络新算法。
The paper comprehensively USES the superiority of fuzzy pattern recognition over rejecting noise information and BP neural network on simulation to design a new algorithm named fuzzy neural network.
把填充函数法与BP算法相结合，提出一种训练前向神经网络的混合型全局优化新算法。
This paper proposes a new global optimization technique in which combines the filled function method and BP algorithm for Training feedforward neural networks.
综合了标准BP算法与“批处理”BP算法的各自特点，提出了一种新的BP网络的学习算法。
To synthesize the advantages of standard BP algorithm and "batch learning" BP algorithm, a new algorithm is put forword.
与标准BP算法比较，该系统通过结合附加动量法和自适应学习速率形成新的BP改进算法。
Compared to the standard BP, this algorithm integrated the additional momentum method with the adaptive learning rate method.
新算法选择很广一类的隐层神经元函数，可以直接求得全局最小点，不存在BP算法的局部极小、收敛速度慢等问题。
The algorithm can get global minimum easily with a wide variety of functions of hidden neurons, and no problems such as local minima and slow rate of convergence are suffered like BP algorithm.
应用BP神经网络算法对工程量清单的分项工程价格进行快速估算，为分项工程单方造价提供新的计算方法和理论。
Abstract: BP network is applied in quick estimation of parts cost of Bill Of Quantity(BOQ) in this paper. It provides a new calculation method and theory for meter cost of sub-project.
新的网络激励函数和训练算法切实满足过程控制的需要。
It is proved that the new network activation function and the improved BP training algorithm practically applying to the requirement of process control.
[摘要]应用BP神经网络算法对工程量清单的分项工程价格进行快速估算，为分项工程单方造价提供新的计算方法和理论。
Abstract: BP network is applied in quick estimation of parts cost of Bill of Quantity (BOQ) in this paper. It provides a new calculation method and theory for meter cost of sub-project.
对BP神经网络算法进行了适当的改进，并提出了几种新的改进方法，得到了良好的效果。
The algorithm of the BP network technique is improved appropriately. Several new improving methods are developed and lead to good results.
提出一种新的神经网络伺服控制器，采用BP网络建立神经网络模型，依据梯度算法建立优化器，可以同时跟踪状态和控制设定变量。
A new design of a neural servocontroller is presented. Neural network model is established by BP network. Optimizer is obtained by gradient descent rule.
在该方法中，提出了一种新的自适应变异操作技术及将遗传算法与BP算法进行自适应切换的实施方案。
A novel adaptive mutation technique and a scheme to shift the training of the network from the GA to the BP algorithm are proposed.
在该方法中，提出了一种新的自适应变异操作技术及将遗传算法与BP算法进行自适应切换的实施方案。
A novel adaptive mutation technique and a scheme to shift the training of the network from the GA to the BP algorithm are proposed.
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