模糊聚类和混沌自适应粒子群的神经网络色彩匹配

目的研究基于混沌理论、粒子群算法、模糊聚类和人工神经网络的色彩匹配模型。方法结合混沌理论和动态自适应策略,对粒子群算法进行改进,得到混沌自适应粒子群算法,并应用于径向基人工神经网络的基函数中心,以及扩展常数和网络权值的优化中;通过模糊聚类分类样本数据,得到混沌自适应粒子群径向基人工神经网络色彩匹配模型,并将模型与其他色彩匹配方法进行比较。结果CSAPSO RBF ANN色彩匹配模型的平均绝对误差、均方根误差和色差平均值分别为0.0106,0.000 96和1.9122。结论 CSAPSO RBF ANN色彩匹配模型具有良好的普遍性、通用性和泛化能力。     

A framework for determining MIMO process parameters by a neuro-DM&ACO approach

A framework combining artificial neural network (ANN) modelling technique, data mining and ant colony optimisation (ACO) algorithm is proposed for determining multiple-input multiple-output (MIMO) process parameters from the initial chemical-mechanical planarisation (CMP) processes used in semiconductor manufacturing. Owing to the invisibility of the ANN in the solution procedures, the decision tree approach of data mining is adopted to provide the necessary information for a real-valued ACO. The simulation result demonstrates that the proposed method can be an efficient tool for selecting properly defined parameter combination with the CMP process.     

基于人工神经网络的Cu-Cr-Zr合金时效强化性能预测研究

本文首次利用神经网络对Cu—Cr—Zr合金时效温度和时间与硬度和导电率样本集进行学习，采用改进的BP网络算法——Levenberg—Marquardt算法，建立了时效强化工艺BP神经网络模型。预测结果表明：该BP神经网络可以充分挖掘样本蕴含的领域知识，可以对材料性能进行有效预测和分析。     

Evolutionary modelling and optimization of grinding processes

The objective of this study is to develop a framework of modelling the complex grinding processes and finding optimal process conditions to meet the general class of process requirements. In order to achieve the above goal, novel modelling schemes and optimization methods based on evolutionary algorithms (EA) are developed. The optimization problem of grinding processes can be formulated as a constrained non-linear programming problem with mixed-discrete variables. The adaptive least-squares (ALS) algorithm proposed by Lee and Shin's 1998 study is extended for modelling multi-input-multi-output (MIMO) complex grinding processes using fuzzy basis function networks (FBFN), while the modified evolution strategies (ES) is proposed for successful optimization of grinding processes. Two grinding optimization problems demonstrate the superior performance of the proposed scheme.     

Machining error compensation using radial basis function network based on CAD/CAM/CAI integration concept

This paper presents a machining error compensation methodology using an Artificial Neural Network (ANN) model trained by an inspection database of the On-Machine-Measurement (OMM) system. This is an application of the CAD/CAM/CAI integration concept. First, to improve machining and inspection accuracies, the geometric errors of a three-axis CNC machining centre and the probing errors are compensated using a closed-loop configuration. Then, a workpiece is machined using the machining centre, and the error distributions of the machined surface are inspected using OMM. In order to analyse efficiently the machining errors, two characteristic error parameters, W err and D err , are defined. Subsequently, these parameters are modelled using a Radial Basis Function (RBF) network approach as an ANN model. Based on the RBF network model, the tool path is corrected to effectively reduce the errors using an iterative algorithm. In the iterative algorithm, the changes of the cutting conditions can be identified according to the corrected tool path. In order to validate the approaches proposed in this paper, an experimental machining process is performed, and the results are evaluated. As a result, about 90% of machining error reduction can be achieved through the proposed approaches.     

Aging process optimization for a copper alloy considering hardness and electrical conductivity

A multi-objective optimization methodology for the aging process parameters is proposed which simultaneously considers the mechanical performance and the electrical conductivity. An optimal model of the aging processes for Cu–Cr–Zr–Mg is constructed using artificial neural networks and genetic algorithms. A supervised artificial neural network (ANN) to model the non-linear relationship between parameters of aging treatment and hardness and conductivity properties is considered for a Cu–Cr–Zr–Mg lead frame alloy. Based on the successfully trained ANN model, a genetic algorithm is adopted as the optimization scheme to optimize the input parameters. The result indicates that an artificial neural network combined with a genetic algorithm is effective for the multi-objective optimization of the aging process parameters.     

Application of artificial intelligence to modelling asphalt–rubber viscosity

The viscosity of binder is of great importance during the handling, mixing, application and compaction of asphalt in highway surfacing. This paper presents experimental data and the application of artificial intelligence techniques (statistics, artificial neural networks (ANNs) and fuzzy logic) to modelling of apparent viscosity in asphalt–rubber binders. The binders were prepared in the laboratory by varying the rubber content (RC), rubber particle size, duration and temperature of mixture in conformity with a statistical design plan. Multi-factorial analysis of variance showed that the RC has a major influence on the viscosity observed for the considered interval of parameters variation. When only limited experimental data of design matrix are available for modelling, the fuzzy logic model is the best model to be used. In addition, the combined use of ANN and multiple regression analysis improved the characteristics of the neural network.     

MCFC型燃料电池的神经网络建模与基于FGA的模糊控制研究

为了提高燃料电池的发电性能,熔融碳酸盐燃料电池(MCFC)堆的运行温度应该控制在一个合适的范围内。本文首先利用RBF神经网络辨识复杂非线性系统的能力,基于实验的输入输出数据,建立起MCFC电堆的神经网络温度模型;然后设计了MCFC电堆工作温度的一个基于模糊遗传算法的在线模糊控制器,用模糊遗传算法同时优化模糊控制器的参数及规则。最后用神经网络的辨识模型代替实际的电堆进行控制仿真,仿真结果证明建模是有效的,所设计的模糊控制器具有较好的性能。     

Structural parameters identification of uncertain multi-storey shear buildings using fuzzy neural network modelling

Present paper proposes a fuzzy neural network (FNN)-based modelling for the identification of structural parameters of uncertain multi-storey shear buildings. Here, the method is developed to identify uncertain structural mass, stiffness and damping matrices from the dynamic responses of the structure without any optimization processes that are generally used to solve inverse vibration problems. Uncertainty has been taken in term of fuzzy numbers. The governing equations of motion are first solved by the classical method to get responses of the consecutive stories. Further the governing equations of motion are modified based on relative responses of consecutive stories in such a way that the new set of equations can be implemented in a cluster of FNNs. As such the model starts solving the nth floor by FNN modelling to estimate the structural parameters. Subsequently, series of FNN models are used to estimate the parameters for (n ? 1)th storey to the first storey. One may note that single layer FNNs have been used for training for each cluster of the FNN such that the converged weights give the uncertain structural parameters. The initial weights in the FNN architecture are taken as the design parameters in uncertain (fuzzy) form. In order to validate the present model, various example problems of different multi-storey shear structures have been considered. Related results are incorporated in term of tables and graphs. Comparisons between theoretical and identified results are carried out and are found to be in good agreement.     

Optimization of chemical composition for TC11 titanium alloy based on artificial neural network and genetic algorithm

It is quite difficult for materials to develop the quantitative model of chemical elements and mechanical properties, because the relationship between them presents the multivariable and non-linear. In this work, the combined approach of artificial neural network (ANN) and genetic algorithm (GA) was employed to synthesize the optimum chemical composition for satisfying mechanical properties for TC11 titanium alloy based on the large amount of experimental data. The chemical elements (Al, Mo, Zr, Si, Fe, C, O, N and H) were chosen as input parameters of the ANN model, and the output parameters are mechanical properties, including ultimate tensile strength, yield strength, elongation and reduction of area. The fitness function for GA was obtained from trained ANN model. It is found that the percentage errors between experimental and predicted are all within 5%, which suggested that the ANN model has excellent generalization capability. The results strongly indicated that the proposed optimization model offers an optimal chemical composition for TC11 titanium alloy, which implies it is a novel and effective approach for optimizing materials chemical composition.     

Analysis and classification of malignancy in pancreatic magnetic resonance images using neural network techniques

Computer-aided diagnosis (CAD) is a computerized way of detecting tumors in MR images. Magnetic resonance imaging (MRI) has been generally used in the diagnosis and detection of pancreatic tumors. In a medical imaging system, soft tissue contrast and noninvasiveness are clear preferences of MRI. Inaccurate detection of tumor and long time consumption are the disadvantages of MRI. Computerized classifiers can greatly renew the diagnosis activity, in terms of both accuracy and time necessity by normal and abnormal images, automatically. This article presents an intelligent, automatic, accurate, and robust method to classify human pancreas MRI images as normal or abnormal in terms of pancreatic tumor. It represents the response of artificial neural network (ANN) and support vector machine (SVM) techniques for pancreatic tumor classification. For this, we extract features from MR images of pancreas using the GLCM method and select the best features using JAFER algorithm. These features are analyzed by five classification techniques: ANN BP, ANN RBF, SVM Linear, SVM Poly, and SVM RBF. We compare the results with benchmark data set of MR brain images. The analytical outcome presents that the two best features used to classify the MR images using ANN BP technique have 98% classification accuracy.     

Self-adaptive radial basis function neural network for short-term electricity price forecasting

Effective and reliable electricity price forecast is essential for market participants in setting up appropriate risk management plans in an electricity market. A reliable price prediction model based on an advanced self-adaptive radial basis function (RBF) neural network is presented. The proposed RBF neural network model is trained by fuzzy c-means and differential evolution is used to auto-configure the structure of networks and obtain the model parameters. With these techniques, the number of neurons, cluster centres and radii of the hidden layer, and the output weights can be automatically calculated efficiently. Meanwhile, the moving window wavelet de-noising technique is introduced to improve the network performance as well. This learning approach is proven to be effective by applying the RBF neural network in predicting of Mackey-Glass chaos time series and forecasting of the electricity regional reference price from the Queensland electricity market of the Australian National Electricity Market.     

Robustness analysis of radial base function and multi-layered feed-forward neural network models

In this paper, two popular types of neural network models (radial base function (RBF) and multi-layered feed-forward (MLF) networks) trained by the generalized delta rule, are tested on their robustness to random errors in input space. A method is proposed to estimate the sensitivity of network outputs to the amplitude of random errors in the input space, sampled from known normal distributions. An additional parameter can be extracted to give a general indication about the bias on the network predictions. The modelling performances of MLF and RBF neural networks have been tested on a variety of simulated function approximation problems. Since the results of the proposed validation method strongly depend on the configuration of the networks and the data used, little can be said about robustness as an intrinsic quality of the neural network model. However, given a data set where ‘pure’ errors from input and output space are specified, the method can be applied to select a neural network model which optimally approximates the nonlinear relations between objects in input and output space. The proposed method has been applied to a nonlinear modelling problem from industrial chemical practice. Since MLF and RBF networks are based on different concepts from biological neural processes, a brief theoretical introduction is given.     

Incentive contract design in project management with serial tasks and uncertain completion times

This article investigates an incentive contract design problem for a project manager who operates a project consisting of multiple tasks performed sequentially by different subcontractors in which all task completion times are uncertain and described by fuzzy variables. On the basis of an expected value criterion and a critical value criterion, two classes of fuzzy bilevel programming models are developed. In the case where the uncertain task completion times are mutually independent, each model can first be decomposed into multiple equivalent sub-models by taking advantage of the structural characteristics, and then a two-step optimization method is employed to derive the optimal incentive contract in each sub-model. In a more general case where the uncertain task completion times are correlative, the approximation approach (AA) technique is adopted first in order to evaluate the objective functions involving fuzzy parameters, which are usually difficult to convert into their crisp equivalents. Then, an AA-based hybrid genetic algorithm integrated with the golden search method and variable neighbourhood search is designed to solve the proposed fuzzy bilevel programming models. Finally, a numerical example of a construction project is conducted to demonstrate the modelling idea and the effectiveness of the proposed methods.     

Dynamic response of structures with uncertain parameters: A comparative study of probabilistic and fuzzy sets models

This paper presents the results of a study on the response of structures with uncertain properties such as mass, stiffness and damping. The effect of the uncertain parameters on the response and the effect of the modelling of the uncertainties on the response are investigated. In particular, two types of uncertainties are distinguished: random and fuzzy uncertainties. Two kinds of models are studied: probabilistic and fuzzy set models. The two approaches to uncertainty modelling are compared with regard to their impacts on the analysis and on the uncertain structural response obtained. The study considers free vibration, forced vibration with deterministic excitation, and forced vibration with Gaussian white noise excitation. It is concluded that, in general, fuzzy models are much easier to implement and the associated analysis easier to perform than their probabilistic counterparts. When the available data on the structural parameters are crude and do not support a rigorous probabilistic model, the fuzzy set approach should be considered in view of its simplicity.     

基于RBF型人工神经网络的碳/陶瓷复合材料的化学成分对硬度的耦合影响分析

用 RBF 型人工神经网络研究了碳/陶瓷复合材料的化学成分对其硬度的影响。首先设计了 RBF 型神经网络模型,用“舍一法”进行了训练,使模型具有满意的预测性能。随后分析了化学组分对硬度的影响,包括单因素影响和双因素耦合影响。结果表明：材料的两种组分同时变化时,对硬度的影响更加复杂,呈现典型的非线性特征。     

Ontology-based neural network for patent knowledge management in design collaboration

In order to stimulate innovation during the collaborative process of new product and production development, especially to avoid duplicating existing techniques or infringing upon others’ patents and intellectual property rights, the collaborative team of research and development, and patent engineers must accurately identify relevant patent knowledge in a timely manner. This research develops a novel knowledge management approach using ontology-based artificial neural network (ANN) algorithm to automatically classify and search knowledge documents stored in huge online patent corpuses. This research focuses on developing a smart and semantic oriented classification and search from the sources of the most critical and well-structured knowledge publications, i.e. patents, to gain valuable and practical references for the collaborative networks of technology-centric product and production development teams. The research uses the domain ontology schema created using Protégé and derives the semantic concept probabilities of key phrases that frequently occur in domain relevant patent documents. Then, by combining the term frequencies and the concept probabilities of key phrases as the ANN inputs, the method shows significant improvement in classification accuracy. In addition, this research provides an advanced semantic-oriented search algorithm to accurately identify related patent documents in the patent knowledge base. The case demonstration analyses 343 chemical mechanical polishing and 150 radio-frequency identification patents sample sets to verify and measure the performance of the proposed approach. The results are compared with the previous automatic classification methods demonstrating much improved outcomes.     

基于RBF神经网络的自动包装机温度控制算法研究

目的针对传统热封工艺中温度调节PID算法参数过度依赖人工经验的缺点,提出一种RBF神经网络与PID算法相结合的具有参数自适应的热封温度控制算法。方法使用控制系统的输出误差作为代价函数,采用最小均方误差(LMS)调整权值与偏置参数,并通过中心自组织算法实现径向基函数中心和中心宽度的动态调节,在Matlab软件中的Simulink子系统中建立仿真模型进行算法验证,并与传统PID控制算法进行比较。结果仿真结果表明,径向基神经网络与传统PID算法的结合使得系统输出响应在动态性能和静态性能方面均优于传统PID,在系统上升时间、调节时间等方面均优于增量式数字PID。结论将RBF神经网络PID算法应用于自动包装机,避免了传统热封工艺中PID控制算法参数不能适应于复杂变换控制环境的问题,神经网络PID算法的自适应性强,实现了热封温度变化下PID参数的自动调整,在一定程度上提升了生产效率和包装设备的智能化水平。     

质量功能展开中关联关系确定的RBF方法

针对质量功能展开中存在顾客需求和工程特性之间的关联关系不确定和模糊等特质,提出了质量功能展开中关联关系的径向基函数(Radial Basis Function,RBF)神经网络方法。采用三层RBF神经网络,由质量屋中顾客需求组成RBF神经网络的输入层神经元,中间层选用高斯核函数,工程特性组成RBF神经网络输出层神经元,由顾客需求和工程特性关联关系评价样本集组成网络的训练样本集,通过网络训练的方式来获得最优的顾客需求与工程特性的关联关系。最后,结合天然光采光产品开发,进行了实例分析,说明该方法具有计算速度快,拟合精度高的特点。     

基于模糊理论的半导体制造系统时变多目标生产调度研究

针对半导体晶圆制造系统(SWFS)调度中的多目标优化和目标时变性问题,提出了一种时变多目标(TVMO)调度算法.基于模糊理论研究了时变模糊集合与多目标重要程度的复杂非线性量化关系,进而研究了SWDS的优化调度方法.所提出的方法兼顾最大产品交期率、最大生产移动量和瓶颈机台最大产能利用率三个目标,根据时变目标权重计算出在制品的加工优先权序列号.大量的仿真实验数据证明,该调度算法能够改善系统多个绩效目标,可为大规模复杂重入型制造系统的科学生产控制与调度提供有效支持.