第二种方法是一种新颖的建模策略，它把晶体生长率作为非线性逼近器的人工神经网络（ANN）与由蔗糖晶体质量平衡所表述的先前既定的知识相结合。
The second is a novel modeling strategy that combines an artificial neural network(ANN)as an approximator of the growth rate with prior knowledge represented by the mass balance of sucrose crystal.
在本文中，生产作业计划人工神经网络模型的研究可分为三个方面：数据处理、优化计算和知识处理。
The research area in production schedule based on artificial neural network includes: data description, optimization computation and knowledge handling.
最后，提出了综合信息融合技术、人工神经网络技术、专家系统知识的智能化异步电动机故障诊断系统。
In the end, the intelligent asynchronous motor fault diagnosis system combining information fusion with artificial network as well as expert knowledge system is presented.
在其推理机方面，应用元知识推理以协调定性推理、定量推理和人工神经网络推理。
In its inference engine, meta-knowledge inference is applied to coordinate with quantitative inference, qualitative inference and neural network inference.
另一种是含有知识人工神经元的人工神经网络(NNKBN)，其知识人工神经元的活化函数是由扩展的经验公式构成。
The other is the neural network with knowledge-based neurons (NNKBN) where extended prior knowledge analytic formulas work as activation functions of the neurons.
将人工神经网络（ANN）、广义猫映射及概率统计等知识相结合构造了一种图像空间域水印算法。
The paper, by adopting jointly Artificial Neural Network (ANN), general Amold mapping, and statistical methods, intends to formulate an algorithm based on image spatial domain watermark.
设计了一个简单的人工智能故障诊断系统模型，它包括知识库、模糊推理、神经网络和控制模块等。
A simple artificial intelligence system for fault diagnosis established in this paper. The system consists of knowledge base, fuzzy reasoning module, neural network module and control module.
提出了基于知识的毛纺产品工艺设计智能模型，通过基于案例（C BR）和基于规则的推理（RBR）以及人工神经网络（ANN）等关键技术的引入，提高了系统解决实际问题的能力。
This paper presents the wool textile process intelligent design model(WTPIDM), by the introduction of CBR, RBR, ANN technologies, improving the system capability to solve the problems.
提出了基于知识的毛纺产品工艺设计智能模型，通过基于案例（C BR）和基于规则的推理（RBR）以及人工神经网络（ANN）等关键技术的引入，提高了系统解决实际问题的能力。
This paper presents the wool textile process intelligent design model(WTPIDM), by the introduction of CBR, RBR, ANN technologies, improving the system capability to solve the problems.
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