基于神经网络的关联规则在故障诊断中的应用

采用加权关联规则算法对设备历史数据库进行挖掘,建立加权关联规则模式库。设备监控数据通过与模式库匹配,实现设备故障诊断。同时,针对钢铁企业中液压设备的特殊性,提出利用自组织竞争神经网络模型确定权值,即将设备故障信息的3个主要属性:重要程度、易损程度、故障等级作为模型的输入,通过训练样本确定设备故障的加权关联规则的权值。实例证明了该方法的有效性。     

神经网络在城市防空地下室防护等级评定中的应用与研究

从城市人防建设实际出发，基于函数型连接神经网络，采用神经网络－专家系统组成的混合系统方法，建立了城市防空地下室防护等级评定系统。根据防空地下室结构特性、地形位置、抗力特性及国防要求，基于给定的防空地下数据参数，经过神经网络的学习、联想、记忆和分类，准确评定出防空地下室防护等级。     

空调系统神经模糊控制器——结构和算法

提出了一种适用于空调系统控制的新型神经模糊控制器。这种神经模糊控制器将神经网络和模糊控制紧密结合，是一种以神经网络表示模糊控制规则的模糊控制系统，控制推理基于模糊推理的精确值法，神经网络采用后向传播（BP）学习算法。本文论述这种神经模糊控制器的结构和算法，其仿真和优化将另文论述。     

敏捷性组织的神经网络描述

敏捷性组织的网络构成具有拓扑性特征。网络工作特征符合神经网络技术的基本假设，本文试用神经网络思想研究敏捷性组织网络的联想记忆模型。     

模糊神经网络及其在高炉参数学习专家系统中的应用

本文应用模糊神经网络对高炉参数学习系统进行研究，该神经网络采用个有线性激励的ＢＰ快速学习算法，能够实现模糊推进进行隶属函数参数和规则可信度的调整，为动态模拟专家系统知识库，本文采用了七个结构相似且相互关联的神经网络代表不同的高炉异常炉况类型。     

动态系统的神经网络辨识方法

本文根据人工神经网络的系统辨识理论，充分发挥神经网络的强大学习功能和记忆联想功能，建立了结构动力系统的计算机仿真模型，并对非线性动态系统和水工结构振动系统进行计算机仿真，仿真结果与实际值具有良好的一致性，这充分说明人工神经网络在结构工程领域的应用价值和深远意义。     

用粗集-模糊神经网络评定空袭目标威胁程度

针对模糊神经网络运算过程中,当模糊规则较多时,网络学习速度慢,方法实时性差的缺点,本文提出采用粗糙集理论对该模型进行优化,该方法利用粗集数据分析方法,通过知识约简从数据中推理逻辑规则,并用约简后规则集作为模糊神经网络的规则将输入映射到输出的子空间上：在这个子空间上用改进的BP算法训练进行逼近。实验结果表明：通过粗集数据挖掘后提取的规则,不仅规则数目减少,且规则是不完全规则,减少了网络输入维数和各层神经元的个数,提高了网络运算速度,满足了系统实时性要求。     

用粗集-模糊神经网络评定空袭目标威胁程度

针对模糊神经网络运算过程中,当模糊规则较多时,网络学习速度慢,方法实时性差的缺点,本文提出采用粗糙集理论对该模型进行优化,该方法利用粗集数据分析方法,通过知识约简从数据中推理逻辑规则,并用约简后规则集作为模糊神经网络的规则将输入映射到输出的子空间上:在这个子空间上用改进的BP算法训练进行逼近.实验结果表明:通过粗集数据挖掘后提取的规则,不仅规则数目减少,且规则是不完全规则,减少了网络输入维数和各层神经元的个数,提高了网络运算速度,满足了系统实时性要求.     

改进型LSTM网络光伏发电功率预测研究

针对现有光伏发电功率预测技术存在的未能充分考虑气象因素、提取特征不充分等导致预测精度较低的问题,基于深度学习理论,提出一种基于改进型LSTM网络的光伏发电功率预测方法。根据长短期记忆神经网络的特点,从循环神经网络(RNN)推导出其一般计算过程,阐述该预测方法的优越性和可行性。提出基于改进型长短期记忆(LSTM)网络的光伏发电率预测模型,该模型充分考虑并优化神经网络带来的过拟合问题,且引入RMSProp算法获取模型最佳的损失函数值,确保得到最佳的预测结果。综合考虑对光伏发电功率产生影响的多种气象因素,并将气象因素做标准化处理后作为模型的初始输入量,在Spyder软件上对预测模型进行仿真验证。最后将上述模型与单一输入因素进行比较,结果显示充分考虑气象因素的预测结果明显优于单一因素,仿真结果证明该模型具有较好的预测精度。     

一种改进BP算法在机械手逆运动学中的应用

通过对传统BP算法的分析，提出了一种改进激励函数的学习方法，并且在神经网络的每一层采用不同的学习速率，以提高训练速度；采用所提出的改进BP算法，训练多层前向神经网络，建立机械手逆运动学模型，仿真结果表明了该算法的有效性；与传统BP算法相比，大大提高了机械手逆运动学的精度。     

Viewpoint: Pavlovian Materials—Functional Biomimetics Inspired by Classical Conditioning

Herein, it is discussed whether the complex biological concepts of (associative) learning can inspire responsive artificial materials. It is argued that classical conditioning, being one of the most elementary forms of learning, inspires algorithmic realizations in synthetic materials, to allow stimuli-responsive materials that learn to respond to a new stimulus, to which they are originally insensitive. Two synthetic model systems coined as “Pavlovian materials” are described, whose stimuli-responsiveness algorithmically mimics programmable associative learning, inspired by classical conditioning. The concepts minimally need a stimulus-triggerable memory, in addition to two stimuli, i.e., the unconditioned and the originally neutral stimuli. Importantly, the concept differs conceptually from the classic stimuli-responsive and shape-memory materials, as, upon association, Pavlovian materials obtain a given response using a new stimulus (the originally neutral one); i.e., the system evolves to a new state. This also enables the functionality to be described by a logic diagram. Ample room for generalization to different stimuli and memory combinations is foreseen, and opportunities to develop future adaptive materials with ever-more intelligent functions are expected.     

Optical associative memory with bipolar edge-enhanced learning that uses a binary spatial light modulator and a BaTiO(3) crystal

An optical associative memory with bipolar edge-enhanced feature learning that uses a ferroelectric liquid-crystal spatial light modulator and a barium titanate crystal is presented. During the learning procedure the bipolar edge-enhanced versions of the patterns are employed, which enable the associative memory to have a high discrimination capability. Experimental results and computer simulations are given.     

Performance analysis and comparison of a minimum interconnections direct storage model with traditional neural bidirectional memories

This study proposes an efficient and improved model of a direct storage bidirectional memory, improved bidirectional associative memory (IBAM), and emphasises the use of nanotechnology for efficient implementation of such large-scale neural network structures at a considerable lower cost reduced complexity, and less area required for implementation. This memory model directly stores the X and Y associated sets of M bipolar binary vectors in the form of (M X N_x) and (M X N_y) memory matrices, requires O(N ) or about 30% of interconnections with weight strength ranging between ±1, and is computationally very efficient as compared to sequential, intraconnected and other bidirectional associative memory (BAM) models of outer-product type that require O(N ²) complex interconnections with weight strength ranging between ±M. It is shown that it is functionally equivalent to and possesses all attributes of a BAM of outerproduct type, and yet it is simple and robust in structure, very large scale integration (VLSI), optical and nanotechnology realisable, modular and expandable neural network bidirectional associative memory model in which the addition or deletion of a pair of vectors does not require changes in the strength of interconnections of the entire memory matrix. The analysis of retrieval process, signal-to-noise ratio, storage capacity and stability of the proposed model as well as of the traditional BAM has been carried out. Constraints on and characteristics of unipolar and bipolar binaries for improved storage and retrieval are discussed. The simulation results show that it has log_e N times higher storage capacity, superior performance, faster convergence and retrieval time, when compared to traditional sequential and intraconnected bidirectional memories.     

Analysis of one-dimensional stochastic finite elements using neural networks

This paper explores the applicability of neural networks for analyzing the uncertainty spread of structural responses under the presence of one-dimensional random fields. Specifically, the neural network is intended to be a partial surrogate of the structural model needed in a Monte Carlo simulation, due to its associative memory properties. The network is trained with some pairs of input and output data obtained by some Monte Carlo simulations and then used in substitution of the finite element solver. In order to minimize the size of the networks, and hence the number of training pairs, the Karhunen–Loéve decomposition is applied as an optimal feature extraction tool. The Monte Carlo samples for training and validation are also generated using this decomposition. The Nyström technique is employed for the numerical solution of the Fredholm integral equation. The radial basis function (RBF) network was selected as the neural device for learning the input/output relationship due to its high accuracy and fast training speed. The analysis shows that this approach constitutes a promising method for stochastic finite element analysis inasmuch as the error with respect to the Monte Carlo simulation is negligible.     

用改进的小脑模型神经网络实现光栅信号连续细分

光栅测量系统常用硬件进行信号细分，存在细分数不高、硬件复杂等问题。为此，研究了一种神经网络细分方法，即利用一种改进的小脑模型神经网络的泛化能力实现光栅信号的连续细分。该小脑模型神经网络采用直接权地址映射技术，将光栅样本信号直接映射到联想存储器，对非样本信号经联想泛化即可实现连续细分。仿真实验结果表明，仅用少量样本即可达到很高的细分精度。     

基于模糊学习算法的空调机组性能模拟

基于模糊熵准则和误差平方和准则建立了模糊学习算法,基于该模糊学习算法,应用BP神经网络对柜式空调机组的性能进行了模拟.结果表明,与传统的基于误差平方和准则的学习算法相比,采用模糊学习算法可以大大简化网络结构,有效提高模拟精度和效率.     

A neural network-based algorithm that searches for the measuring points of a rule surface

The purpose of this study is to use a proposed neural network-based algorithm to explore the determination of the recommended measuring points for a rule surface. The task of measuring a rule surface starts from the rule surface design blueprint. Mesh grid data on the designed rule surface were selected. The pattern recognition capability of the back-propagation neural network is explored in this article. The network learning was successfully performed by the learning and testing of the network, the support of a designated acceptable perpendicular error value, a learning model in which training examples were gradually added and the adjustment of the number of training examples according to the network structure.     

Tabu search techniques

Summary Tabu Search is a general heuristic procedure for global optimization. Based on simple ideas it has been extremely efficient in getting almost optimal solutions for many types of difficult combinatorial optimization problems.The principles of Tabu Search are discribed and illustrations are given. An example of problem type where the use of Tabu Search has drastically cut down the computational effort is presented; it consists of the learning process of an associative memory represented by a neural network.

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Zusammenfassung Tabu Search ist eine heuristische Methode, die für globale Optimierung mit viel Erfolg in verschiedenen Umständen angewandt wurde.Die Grundideen der Methode werden erklärt und mit Beispielen illustriert. Eine Anwendung an ein Lernprozess im Gebiet der Neuronen Netzwerke wird beschrieben.

     

Learning and recall algorithm for optical associative memory using a bistable spatial light modulator

A learning and recall algorithm for optical associative memory based on the conventional correlationlearning method with three effective improvements (sparse-encoding method, constant-total-activity method, and binary memory) is proposed from a viewpoint of practical implementation. It is shown that the algorithm is suitable for implementation with a bistable spatial light modulator such as a ferroelectric liquid-crystal spatial light modulator, which has high resolution and a fast response time. The results of theoretical analysis and simulations indicate that the algorithm permits an associative-memory system with a large memory capacity to be realized. An example of an optical system for executing this algorithm is proposed. To determine the performance specifications that are required for the various optical components within the system, we simulate and evaluate the effect of noise (which is caused by nonideal components) on system performance. These results show that the system is robust in the presence of predicted noise levels.