基于人工神经网络的葡萄病害诊断专家系统

设计了一种基于人工神经网络的葡萄病害诊断专家系统。以常见的18种主要的葡萄病害为研究对象,将专家知识转换为诊断规则,并作为学习样本输入神经网络进行训练,形成人工神经网络推理机。同时,采用知识库、规则推理和人工神经网络推理相结合的系统结构来优化专家系统,在提高专家系统自学能力的同时也提高了系统的响应速度。采用C#、Matlab和.NET技术混合编程实现专家系统,实验结果表明该系统有较高的诊断准确率并能稳定运行。该系统在Web上运行,更有利于系统的推广应用。     

矿渣微晶玻璃神经网络专家系统的应用

矿渣微晶玻璃以废渣和工业尾矿为主要原料,是一种对环境友好的建筑材料。但是由于矿渣微晶玻璃组成、工艺和性能之间关系仍不明确,其新材料的开发仍然采用“炒菜法”,效率低下。我们根据材料设计思想,建立了基于人工神经网络的矿渣微晶玻璃材料设计专家系统。本文详细介绍了矿渣微晶玻璃神经网络专家系统进行材料设计的总体运行过程,并具体说明了系统主界面、神经网络模块和优化模块的界面组成和应用。用实例检验了系统的运行效果,结果表明系统中已经建立的各种模型和知识库已经可以满足一般矿渣微晶玻璃材料设计问题的需求。     

Real-time penetration state monitoring using convolutional neural network for laser welding of tailor rolled blanks

In this paper, an innovative monitoring system capable of diagnosing the penetration state during the laser welding process is introduced, which consists of two main blocks: a coaxial visual monitoring platform and a penetration state diagnosis unit. The platform can capture coaxial images of the interaction zone during the laser welding through a partially transmitting mirror and a high-speed camera. An image dataset representing four welding states was created for training and validation. The unit mainly consists of an embedded power-efficient computing TX2 and image processing algorithms based on a convolution neural network (CNN). Experiment results show that the platform can stably capture state-of-the-art welding images. The CNN used for a diagnosis of the penetration state is optimized using an optimal network structure and hyperparameters, applying a super-Gaussian function to initialize the weights of the convolutional layer. Its latency on TX2 is less than 2 ms, satisfying the real-time requirement. During the real laser welding of tailor-rolled blanks, a penetration state diagnosis with an accuracy of 94.6 % can be achieved even if the illumination changes significantly. The similar accuracy between the validating set and a real laser welding demonstrates that the proposed monitoring system has strong robustness. The precision and recall ratios of the CNN are higher than those of other methods such as a histogram of oriented gradients and local binary pattern.     

An expert system for detection of breast cancer based on association rules and neural network

This paper presents an automatic diagnosis system for detecting breast cancer based on association rules (AR) and neural network (NN). In this study, AR is used for reducing the dimension of breast cancer database and NN is used for intelligent classification. The proposed AR + NN system performance is compared with NN model. The dimension of input feature space is reduced from nine to four by using AR. In test stage, 3-fold cross validation method was applied to the Wisconsin breast cancer database to evaluate the proposed system performances. The correct classification rate of proposed system is 95.6%. This research demonstrated that the AR can be used for reducing the dimension of feature space and proposed AR + NN model can be used to obtain fast automatic diagnostic systems for other diseases.     

An expert system based on wavelet transform and radon neural network for pavement distress classification

Nowadays, pavement distresses classification becomes more important, as the computational power increases. Recently, multi-resolution analysis such as wavelet decompositions provides very good multi-resolution analytical tools for different scales of pavement analysis and distresses classification. In this paper an expert system is proposed for pavement distress classification. A radon neural network, based on wavelet transform expert system is used for increasing the effectiveness of the scale invariant feature extraction algorithm. Wavelet modulus is calculated and Radon transform is then applied to the wavelet modulus. The features and parameters of the peaks are finally used for training and testing the neural network. Experimental results demonstrate that the proposed expert system is an effective method for pavement distress classification. The test performances of this study show the advantages of proposed expert system: it is rapid, easy to operate, and have simple structure.     

Case-based reasoning and neural network based expert system for personalization

We suggest a hybrid expert system of case-based reasoning (CBR) and neural network (NN) for symbolic domain. In previous research, we proposed a hybrid system of memory and neural network based learning. In the system, the feature weights are extracted from the trained neural network, and used to improve retrieval accuracy of case-based reasoning. However, this system has worked best in domains in which all features had numeric values. When the feature values are symbolic, nearest neighbor methods typically resort to much simpler metrics, such as counting the features that match. A more sophisticated treatment of the feature space is required in symbolic domains.We propose feature-weighted CBR with neural network, which uses value difference metric (VDM) as distance function for symbolic features. In our system, the feature weight set calculated from the trained neural network plays the core role in connecting both the learning strategies. Moreover, the explanation on prediction can be given by presenting the most similar cases from the case base. To validate our system, illustrative experimental results are presented. We use datasets from the UCI machine learning archive for experiments. Finally, we present an application with a personalized counseling system for cosmetic industry whose questionnaires have symbolic features. Feature-weighted CBR with neural network predicts the five elements, which show customers’ character and physical constitution, with relatively high accuracy and expert system for personalization recommends personalized make-up style, color, life style and products.     

An expert system based on S-transform and neural network for automatic classification of power quality disturbances

In this paper, an S-transform-based neural network structure is presented for automatic classification of power quality disturbances. The S-transform (ST) technique is integrated with neural network (NN) model with multi-layer perceptron to construct the classifier. Firstly, the performance of ST is shown for detecting and localizing the disturbances by visual inspection. Then, ST technique is used to extract the significant features of distorted signal. In addition, an optimum combination of the most useful features is identified for increasing the accuracy of classification. Features extracted by using the S-transform are applied as input to NN for automatic classification of the power quality (PQ) disturbances that solves a relatively complex problem. Six single disturbances and two complex disturbances as well pure sine (normal) selected as reference are considered for the classification. Sensitivity of proposed expert system under different noise conditions is investigated. The analysis and results show that the classifier can effectively classify different PQ disturbances.     

一种改进的神经网络机械故障诊断专家系统

针对传统BP神经网络训练中收敛速度较慢的缺点,提出一种基于L-M算法的神经网络应用于机械设备故障诊断的专家系统。论述了神经网络的专家系统结构,并以7216圆锥轴承试验研究为例,建立了基于该算法的故障诊断模型。仿真结果表明:该模型显著缩短了训练时间,具有较高的准确性。运用该神经网络专家系统进行机械故障诊断是有效的。     

An artificial neural network based decision support system for energy efficient ship operations

Reducing fuel consumption of ships against volatile fuel prices and greenhouse gas emissions resulted from international shipping are the challenges that the industry faces today. The potential for fuel savings is possible for new builds, as well as for existing ships through increased energy efficiency measures; technical and operational respectively. The limitations of implementing technical measures increase the potential of operational measures for energy efficient ship operations. Ship owners and operators need to rationalise their energy use and produce energy efficient solutions. Reducing the speed of the ship is the most efficient method in terms of fuel economy and environmental impact. The aim of this paper is twofold: (i) predict ship fuel consumption for various operational conditions through an inexact method, Artificial Neural Network ANN; (ii) develop a decision support system (DSS) employing ANN-based fuel prediction model to be used on-board ships on a real time basis for energy efficient ship operations. The fuel prediction model uses operating data – ‘Noon Data’ – which provides information on a ship’s daily fuel consumption. The parameters considered for fuel prediction are ship speed, revolutions per minute (RPM), mean draft, trim, cargo quantity on board, wind and sea effects, in which output data of ANN is fuel consumption. The performance of the ANN is compared with multiple regression analysis (MR), a widely used surface fitting method, and its superiority is confirmed. The developed DSS is exemplified with two scenarios, and it can be concluded that it has a promising potential to provide strategic approach when ship operators have to make their decisions at an operational level considering both the economic and environmental aspects.     

基于神经网络的北京市水体水华短期预报系统

采用算法改进型的BP神经网络,选择叶绿素含量、磷、氮磷比、电导率和水温五个参数作为模型输入,以预测1日、3日和5日后的叶绿素含量为目标,构建了北京市长河水系水华短期预报系统。该系统三个周期的预测精度分别达到了97.2%、94%、88.3%,并且具有较好的泛化能力。相比于其它智能算法,BP神经网络结构简单、方便实用,仍然具有很强的应用性。     

An expert system based on wavelet decomposition and neural network for modeling Chua’s circuit

This paper presents an expert system based on wavelet decomposition and neural network for modeling and simulation of Chua’s circuit which is used for chaos studies. The problems which arise in modeling Chua’s circuit by neural networks are high structural complexity and slow and difficult training. With this proposed method a new solutions is produced to solve these problems. Wavelet decomposition is used for new useful feature extracting from input signal and neural network is used for modeling. Test results of proposed wavelet decomposition and neural network model are compared with test results of neural network model. Desired performance is provided by this new model. Test results showed that the suggested method can be used efficiently for modeling nonlinear dynamical systems.     

An artificial neural network for predicting the friction coefficient of deposited Cr1−xAlxC films

This paper applies a generalized regression neural network (GRNN) for predicting the friction coefficient of deposited Cr_1−xAl_xC films on high-speed steel substrates via direct current magnetron sputtering systems. The Cr_1−xAl_xC films exhibited some excellent characteristics, such as low friction coefficient, high hardness, and large contact angle. In this study, a GRNN model is applied for predicting the friction coefficient of Cr_1−xAl_xC films on high-speed steel substrates instead of complex practical experiments. The results exhibit good prediction accuracy of friction coefficient since about ±0.97% average errors and show the feasibility of the prediction model. Compared to the conventional back propagation model, the GRNN model is more suitable to predict the friction coefficient of Cr_1−xAl_xC films.     

基于RBF神经网络专家系统的断路器故障诊断

设计了基于RBF神经网络的故障诊断专家系统,克服了在知识获取和表达上的薄弱环节,只需要领域专家解决问题的实例或范例来训练神经网络,使其在同样输入的情况下神经网络能够得到与专家给出的解答尽可能相同的输出。将测试到的结果进行分析,从结果中可以得知此系统运行状态,断路器是否出现故障及其故障类型,并根据其产生的故障原理,针对出现的问题进行相应的操作。     

基于神经网络的软件水印实现方案

本文总结了现有的软件水印算法并给出了水印系统的形式化定义,继而提出一种新的基于神经网络的软件水印实现方案,并对其进行了简单的性能分析。     

基于人工神经网络的智能诊断系统(NNIDS)

本文论述了一个基于人工神经网络 (ArtificialNeuralNetworks)的智能诊断系统 (NNIDS)的设计思想、总体结构及实现的基本原理 ,为解决智能诊断问题提供了一种有效途径。系统具有知识自动获取、识别速度快、橹棒性及容错能力强等特点。     

An artificial neural network based heuristic for flow shop scheduling problems

The objective of this paper is to find a sequence of jobs in the flow shop to minimize makespan. A feed forward back propagation neural network is used to solve the problem. The network is trained with the optimal sequences of completely enumerated five, six and seven jobs, ten machine problem and this trained network is then used to solve the problem with greater number of jobs. The sequence obtained using artificial neural network (ANN) is given as the initial sequence to a heuristic proposed by Suliman and also to genetic algorithm (GA) as one of the sequences of the population for further improvement. The approaches are referred as ANN-Suliman heuristic and ANN-GA heuristic respectively. Makespan of the sequences obtained by these heuristics are compared with the makespan of the sequences obtained using the heuristic proposed by Nawaz, Enscore and Ham (NEH) and Suliman Heuristic initialized with Campbell Dudek and Smith (CDS) heuristic called as CDS-Suliman approach. It is found that the ANN-GA and ANN-Suliman heuristic approaches perform better than NEH and CDS-Suliman heuristics for the problems considered.     

基于BP神经网络的水牛疾病诊断系统

针对传统专家系统获取知识困难自学习能力差和推理能力弱等缺点,设计并实现了基于BP神经网络的水牛疾病诊断专家系统.系统用专家以往诊断水牛疾病的病历来训练神经网络,并通过训练过的神经网络来实现疾病的诊断,然后针对诊断结果进行反向推理以确诊水牛疾病,结果表明,该系统很好地改进了传统专家系统存在的一些缺点.另外,该水牛疾病诊断系统的实现方法可推广应用到其它动物的疾病诊断系统中,为疾病诊断系统的开发提供了一条有效的新途径.     

基于人工神经网络的秀丽隐杆线虫趋温性行为的建模与仿真

为了模拟秀丽隐杆线虫的趋温性行为,提出一种通过人工神经网络对秀丽隐杆线虫的趋温性行为进行建模的方法,并进行实验仿真。首先,建立秀丽隐杆线虫的运动模型;然后,通过设计非线性函数逼近线虫趋温性的运动逻辑,实现运动速度和偏向角度的改变功能;最后,通过人工神经网络对该非线性函数进行学习,从而在Matlab环境中对上述过程进行实验仿真,模拟出了秀丽隐杆线虫的趋温性行为。实验结果表明,在更接近生物体本质的条件下,反馈（BP）神经网络比径向基函数（RBF）神经网络能更好地模拟线虫的趋温性行为。同时也表明所提方法能够很好地模拟秀丽隐杆线虫的趋温性行为,在一定程度上揭示了线虫趋温性的实质,理论上支持了爬虫机器人的趋温性研究。     

用人工神经网络实现秘密共享

以人工神经网络为基础实现了一种秘密共享方案.该方案不同于已有的一些秘密共享方案,它利用人工神经网络分类、识别的性质,将参与恢复秘密的用户组合类比为人工神经网络的输入序列,通过训练,人工神经网络可以识别正确的用户组合并得到原始秘密.该秘密共享方案可以实现不同权限的用户所参与的秘密共享方案,但是不会增加存储或计算上的开销.