基于模糊神经网络的智能火灾报警控制系统

介绍了基于模糊神经网络智能火灾报警控制系统．在系统中应用了模糊控制理论既提高了系统的精度又最大限度的减少了系统的误报率，解决了火灾报警系统中长期存在的问题。结合具有自学习功能的神经网络算法，提高了系统的智能化程度，是现代智能控制理论在消防自动化系统中的应用。     

基于模糊神经网络的智能火灾报警系统

针对目前建筑物发生火灾事故后果严重、损失大的问题,本文主要设计了基于模糊神经网络的智能火灾报警系统.采用模糊控制理论可以提高报警系统的灵敏度,减少误报率;结合神经网络的自学习功能可以提高整个系统的智能化程度.整个火灾报警系统采用分布智能型结构.     

模糊神经网络技术在火灾探测信号处理中的应用

本文结合火灾探测信号的特点,提出一种将模糊神经网络应用于火灾探测报警系统的方法,实现模糊神经网络系统的自学习和自适应功能.依据模糊神经网络算法的要求,完成了网络结构的设计,并用BP算法对网络进行学习和训练.有效提高火灾报警的准确性,减少了火灾报警系统漏报率和误报率.     

模糊神经网络技术在火灾探测信号处理中的应用

本文结合火灾探测信号的特点,提出一种将模糊神经网络应用于火灾探测报警系统的方法,实现模糊神经网络系统的自学习和自适应功能。依据模糊神经网络算法的要求,完成了网络结构的设计,并用BP算法对网络进行学习和训练。有效提高火灾报警的准确性,减少了火灾报警系统漏报率和误报率。     

基于模糊神经网络的火灾探测系统研究

为降低火灾报警误报、漏报频率,根据火灾探测特点,提出了基于模糊神经网络的火灾探测系统设计方案,介绍了基于模糊神经网络的火灾探测系统组成、模糊神经网络的设计与应用等。该系统可对传感器探测到的信息进行智能化处理,明显提高了火灾探测的灵活性和准确性。仿真结果证明了该方法的可行性。     

基于模糊神经网络的智能火灾报警系统

本文介绍了基于模糊神经网络智能火灾报警系统．在系统中应用了模糊控制理论既提高了系统的精度又最大限度的减少了系统的误报率，解决了火灾报警系统中长期存在的问题，结合具有自学习功能的神经网络算法，提高了系统的智能化程度，是现代智能控制理论存消防自动化系统中的应用。     

基于模糊神经网络的火灾信号处理设计

本文针对传统智能建筑中火灾报警系统功能简单、定位困难且存在误报和漏报等问题,设计了一种基于模糊控制理论、人工神经网络的火灾安全报警系统。该系统依靠模糊控制理论提高了灵敏度,减少了误报率,并结合神经网络具有自学习功能的特点,提高了整个系统的智能化水平。作者阐述了火灾报警系统的设计原理,对模糊理论和几种神经网络模型进行了分析,并使用MATLAB软件对设计的算法进行了仿真分析。该系统达到了比较理想的效果。     

智能型模糊神经网络火灾报警系统的研究

针对目前我国消防系统中存在的不足,提出了一种基于模糊神经网络的智能型火灾报警系统。该应用系统结合信息融合技术,大大提高了系统的柔韧性,并应用智能控制、模糊神经网络方法;给出了具体的实现方式。     

智能火灾两级报警与联动控制系统的设计

设计了一种基于AT89C55单片机的智能火灾两级报警与联动控制系统。此系统结合多种火灾因素探测器和运用模糊神经网络算法来提高报警的可靠性,采用两级报警和主控中心联动灭火的设计提高报警和自动灭火的及时性。分别说明了系统的硬件电路设计、系统的通讯方式和软件设计思想。这一设计可提高火灾报警系统的可靠性与及时性,减少误报率,提高系统的智能化程度。     

神经网络模糊推理系统在火灾探测中的应用

提出了一种用于火灾自动探测的神经网络模糊推理系统，它采用前馈神经网络对火灾探测器信号进行处理，神经网络输出的火灾概率经模糊推理系统判决，输出火灾报警信号。这种方法结合了神经网络和模糊逻辑的优点，实验表明这种系统能够准确探测火灾并减少了误报警。     

一种基于模糊B样条基函数神经网络控制的磨削加工系统

本文将模糊控制与神经网络相结合。用神经网络来实现模糊推理,提出了一种把B样条函数作为隶属函数的模糊神经网络,并将之用于磨削加工的质量控制。仿真结果表明,该系统具有响应快、稳态精度高、鲁棒性强等优点,能很好地应用于磨削加工的质量控制。     

A Fuzzy Inference Network Model for Search Strategy Using Neural Logic Network

In this paper, a fuzzy inference network model for search strategy using neural logic network is presented. The model describes search strategy, and neural logic network is used to search. Fuzzy logic can bring about appropriate inference results by ignoring some information in the reasoning process. Neural logic networks are powerful tools for the reasoning process but not appropriate for the logical reasoning. To model human knowledge, besides the reasoning process capability, the logical reasoning capability is equally important. Another new neural network called neural logic network is able to do the logical reasoning. Because the fuzzy inference is a fuzzy logical reasoning, we construct a fuzzy inference network model based on the neural logic network, extending the existing rule inference network. And the traditional propagation rule is modified.     

基于补偿神经网络的航空电子故障智能诊断系统及应用

针对航空电子系统中故障诊断的问题，提出一种将神经网络中的BP算法与模糊逻辑系统相结合、自动产生并自动修正模糊规则的自适应的模糊逻辑推理机。通过函数逼近仿真分析和航空电子系统故障诊断的实际应用，证明此方法简单有效，故障诊断的精度高，取得了较好的效果，具有一定的应用前景。     

基于ANFIS的锅炉蒸汽温度预测控制系统

针对锅炉主蒸汽温度对象的不确定性与大纯滞后等特性 ,提出了一种用BP网络 ,进行建模与预测 ,用自适应网络模糊推理系统 (ANFIS)实施控制的智能控制方案 ,利用Matlab软件仿真表明 ,该算法鲁棒性好 ,抗干扰能力强 ,能有效的补偿对象的大纯滞后     

基于模糊神经网络的导弹故障诊断专家系统

为了实现对导弹测发控系统的故障诊断,研究了模糊神经网络理论与算法,以及和专家系统的结合方式;综合神经网络、专家系统和模糊逻辑的各自优点和特点,提出了构建基于模糊神经网络的故障诊断专家系统的基本原则,并给出了一种构建方法;通过将传统的专家系统技术与模糊神经网络技术相融合,文中构造了某型导弹测发控系统智能故障诊断系统,验证了方案的可行性,为类似系统的进一步实现进行了有益的探索。     

Evaluation of peak and residual conditions of actively confined concrete using neuro-fuzzy and neural computing techniques

This paper investigates the ability of four artificial intelligence techniques, including artificial neural network (ANN), radial basis neural network (RBNN), adaptive neuro-fuzzy inference system (ANFIS) with grid partitioning, and ANFIS with fuzzy c-means clustering, to predict the peak and residual conditions of actively confined concrete. A large experimental test database that consists of 377 axial compression test results of actively confined concrete specimens was assembled from the published literature, and it was used to train, test, and validate the four models proposed in this paper using the mentioned artificial intelligence techniques. The results show that all of the neural network and ANFIS models fit well with the experimental results, and they outperform the conventional models. Among the artificial intelligence models investigated, RBNN model is found to be the most accurate to predict the peak and residual conditions of actively confined concrete. The predictions of each proposed model are subsequently used to study the interdependence of critical parameters and their influence on the behavior of actively confined concrete.

     

Fuzzy neural networks for estimation and fuzzy controller design: simulation study for a pulp batch digester

A structural implementation of a fuzzy inference system through connectionist network based on MLP with logical neurons connected through binary and numerical weights is considered. The resulting fuzzy neural network is trained using classical backpropagation to learn the rules of inference of a fuzzy system, by adjustment of the numerical weights. For controller design, training is carried out off line in a closed loop simulation. Rules for the fuzzy logic controller are extracted from the network by interpreting the consequence weights as measure of confidence of the underlying rule. The framework is used in a simulation study for estimation and control of a pulp batch digester. The controlled variable, the Kappa number, a measure of lignin content in the pulp, which is not measurable is estimated through temperature and liquor concentration using the fuzzy neural network. On the other hand a fuzzy neural network is trained to control the Kappa number and rules are extracted from the trained network to construct a fuzzy logic controller.     

Application of Fuzzy Neural Network to Fire Alarm System of High-rise Building

Fire alarm system is important in high-rise building. In this paper, a fire alarm system of high-rise building is designed using fuzzy system theory and neural network. The fuzzy system has superiority in inference and the neural network has superiority in learning. The design parameters of the fuzzy system can b~ adjusted automatically by combining the fuzzy system with the neural network. The contradiction between alarm sensitivity and false alarm rate is solved by intelligently monitoring of the temperature and aerosol, which are detected by temperature sensor and aerosol sensor in the system.     

漂浮基双臂空间机器人系统的模糊神经网络自学习控制

讨论了载体位置、姿态均不受控制的情况下自由漂浮双臂空间机器人系统的高斯基模糊神经网络自 学习控制问题．此类空间机器人系统严格遵守动量守恒和角动量守恒,所以其动力学方程表现出强烈的非线性性 质．将神经网络与模糊控制相结合,即利用神经网络进行模糊推理, 可使模糊控制具有自学习能力．在此基础上, 设计了双臂空间机器人系统关节空间的高斯基模糊神经网络自学习控制方案．系统的数值仿真证实了该方法的有 效性．     

模糊神经网络变幅液压系统故障诊断

针对变幅液压系统复杂性、不确定性、模糊性的特点,提出基于故障树的模糊神经网络作为变幅液压系统故障诊断的方法。该方法利用故障树知识提取变幅液压系统故障诊断的输入变量和输出变量,引入模糊逻辑的概念,采用模糊隶属函数来描述这些故障的程度,利用Levenberg-Marquardt优化算法对神经网络进行训练,系统推理速度快,容错能力强,并通过实例分析验证了变幅液压系统模糊神经网络故障诊断的有效性。