模式识别成为人工神经网络特别适宜求解的一类问题。
Artificial neural network pattern recognition has become especially suitable for solving a class of problem.
第三类观点以联结和人工神经网络概念为要素，分别探讨了心理场距离和规避损失偏向对选择偏好的影响。
The third one, based on concepts of connectionist and artificial neural network, explored the effects of field distance and loss aversion bias respectively on choice preference.
椭球单元通过高斯分布逼近形成各模式类的决策区域，是一种非常适合于模式识别任务的前馈型人工神经网络模型。
Neural Networks with Ellipsoidal Activation Functions closes in upon a decision making region by Gauss distribution for various patterns and is adapted to fault diagnosis well.
本文根据实例采用人工神经网络方法就多因素综合判别膨胀土类级问题进行了研究，取得了满意的解估计。
The paper adopts the method of manual neural network which study many factors integrative estimate grades of swelling soils in terms of example, the result is very good.
而神经网络则是用工程技术手段模拟生物神经网络的结构特征和功能特征的一类人工系统。
Artificial neural network is one of artificial system, which simulates structural and functional characteristics of biological neural network resorted to engineering technological means.
高速数据传输中信道均衡算法大致分为三类：即基于LMS准则、LS准则和人工神经网络的自适应算法。
Algorithms currently used for high speed data transmission over voice-band channels can be based on following three principles: on LMS criterion, on ls criterion or on artificial neural network.
讨论了一类二元时滞反馈人工神经网络模型。
This paper is concerned with a two-neuron artificial neural network model with delayed feedback.
本文研究了一类二元离散人工神经网络模型的解的收敛性及周期解的存在性等动力学特征。
This thesis has studied the dynamic features of a class of the discrete-time neural network model of two neurons, such as the convergence and periodicity and etc.
人工神经网络是简单的人工神经元的简单聚类。
Artificial neural networks are the simple clustering of the primitive artificial neurons.
基于人工神经网络与模糊控制理论，对非线性系统提出了一种自组织模糊神经网络模型，并推导出一类新型学习算法。
Based on a neural network and the fuzzy control theory, this paper presents a self-organizing fuzzy-neural network for nonlinear systems, and develops a new learning algorithm.
基于人工神经网络与模糊控制理论，对非线性系统提出了一种自组织模糊神经网络模型，并推导出一类新型学习算法。
Based on a neural network and the fuzzy control theory, this paper presents a self-organizing fuzzy-neural network for nonlinear systems, and develops a new learning algorithm.
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