提出一种关于多层前向神经网络结构的混沌优化设计方法。
The optimization design method is proposed for feed-forward neural network structure by means of chaos ergodicity and randomicity.
在应用人工神经网络时，采用基于相关分析法的节点删除法来优化网络结构提高网络性能。
During the use of the artificial neural network some nodes were deleted to optimize the network based on the correlation analytical theory.
由于动态神经网络结构及权值确定困难，采用二进制与实数编码相结合的联合编码，用遗传算法优化得到神经网络结构及对应权值。
To rise above the difficulty of determining NN's structure and weights, the GA optimization algorithm is used to get them by combining binary encoding with real encoding.
文章提出了一种新的优化神经网络结构及权值的方法——两级进化的方法。
This paper proposes a new optimizing neural network structure and weight distribution method two-step evolutionary method.
然后文章提出用遗传算法来优化神经网络结构，从而从整体上提升疾病诊断模型的性能。
Acordingly, it proposed that optimize the structure of neural network using genetic algorithms, so as to enhance the overall performance of disease diagnostic model.
修剪法是确定和优化神经网络结构的重要方法之一。
Pruning algorithm is an important method to set up and optimize the structure of neural network model.
神经网络结构及权值优化属两级进化方法。
Simultaneity optimization for structure and weight distribution of neural network belongs to two class evolutionary method.
本文研究了模糊神经网络，用神经网络结构进行模糊推理，用BP算法调节和优化具有局部性的参数。
In this paper, fuzzy neural network was studied and fuzzy reasoning was realized by use of neural networks structure. BP algorithm is used to optimize local parameter.
针对神经网络的结构存在冗余的问题，提出了一种利用粗糙集优化神经网络结构的方法。
In allusion to the redundancy of neural network structure, a optimizing method of neural network structure based on rough sets is established.
在RBF神经网络中采用了一种减聚类的学习算法来确定径向基函数的相应参数，从而使神经网络结构得到优化。
A learning algorithm of subtractive clustering method for RBFNN is used to obtain the parameters of radial basis function, so that RBFNN has an optimized structure.
本文提出了改进的RBF神经网络结构、核函数个数、位置与宽度优化算法。
This paper presents an adaptive algorithm of optimally determining the structures, number, positions and widths of kernel functions of the improved radial basis function (IRBF) neural networks.
研究了灰色系统理论与神经网络组合的灰色神经网络GNNM（1，1）模型的建模思想、网络结构及其优化GNNM（1，1）模型的方法和学习算法；
Study modelling thought, network configuration, majorize GNNM(1,1) mode method and learning algorithm of GNNM(1,1) mode combined grey system theory and neural network.
最后，对BP神经网络的训练目标、网络结构和传递函数等参数进行了优化，初步实现对镰刀菌的分类。
In the end, we initially achieved the classification for Fusarium by optimizing BP neural network training targets, network structure and transfer function.
最后，对BP神经网络的训练目标、网络结构和传递函数等参数进行了优化，初步实现对镰刀菌的分类。
In the end, we initially achieved the classification for Fusarium by optimizing BP neural network training targets, network structure and transfer function.
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