Using a Neural Network to Enhance the Decision Making Quality of a Visual Interactive Simulation Model

The use of a neural network to represent the results of a simulation model is described. The neural network is implemented as an interaction within a visual interactive simulation model. All results obtained from the simulation are offered to the neural network. After a suitable period of training the quality of results obtained from the network matches those obtained by running the original simulation model. An example which embeds a neural network as an interaction within a visual interactive simulation model is described. The example shows how the combined system may enhance the decision making quality of a visual interactive simulation model.     

A comparison of factorial and random experimental design methods for the development of regression and neural network simulation metamodels

This paper compares two forms of experimental design methods that may be used for the development of regression and neural network simulation metamodels. The experimental designs considered are full factorial designs and random designs. The paper shows that, for two example problems, neural network metamodels using a randomised experimental design produce more accurate and efficient metamodels than those produced by similar sized factorial designs with either regression or neural networks. The metamodelling techniques are compared by their ability to predict the results from two manufacturing systems that have different levels of complexity. The results of the comparison suggest that neural network metamodels outperform conventional regression metamodels, especially when data sets based on randomised simulation experimental designs are used to produce the metamodels rather than data sets from similar sized full factorial experimental designs.     

Optimization of manufacturing systems using a neural network metamodel with a new training approach

In this study, two manufacturing systems, a kanban-controlled system and a multi-stage, multi-server production line in a diamond tool production system, are optimized utilizing neural network metamodels (tst_NNM) trained via tabu search (TS) which was developed previously by the authors. The most widely used training algorithm for neural networks has been back propagation which is based on a gradient technique that requires significant computational effort. To deal with the major shortcomings of back propagation (BP) such as the tendency to converge to a local optimal and a slow convergence rate, the TS metaheuristic method is used for the training of artificial neural networks to improve the performance of the metamodelling approach. The metamodels are analysed based on their ability to predict simulation results versus traditional neural network metamodels that have been trained by BP algorithm (bp_NNM). Computational results show that tst_NNM is superior to bp_NNM for both of the manufacturing systems.     

基于DEA和神经网络集成模型的我国基础设施投资有效性预测研究

建立了DEA和神经网络集成的基础设施投资有效性预测模型。该模型首先应用DEA方法,对我国1993-2007年逐期的基础设施投资效率进行评价,得到了用于基础设施投资有效性预测的基本数据。根据对评价结果的投资有效和无效划分建立预测样本,选择多层感知器神经网络,分别对基础设施的规模有效性和技术有效性进行了预测。结果表明基础设施的投资有效性预测具有可行性,而且通过与RBF神经网络、logistic回归和C-支持向量分类机等方法对比,MLP-NN方法的回应率和反查都具有优势,表明应用DEA-MLP-NN进行有效性预测更为有效。     

A hybrid intelligent algorithm for portfolio selection problem with fuzzy returns

Portfolio selection theory with fuzzy returns has been well developed and widely applied. Within the framework of credibility theory, several fuzzy portfolio selection models have been proposed such as mean–variance model, entropy optimization model, chance constrained programming model and so on. In order to solve these nonlinear optimization models, a hybrid intelligent algorithm is designed by integrating simulated annealing algorithm, neural network and fuzzy simulation techniques, where the neural network is used to approximate the expected value and variance for fuzzy returns and the fuzzy simulation is used to generate the training data for neural network. Since these models are used to be solved by genetic algorithm, some comparisons between the hybrid intelligent algorithm and genetic algorithm are given in terms of numerical examples, which imply that the hybrid intelligent algorithm is robust and more effective. In particular, it reduces the running time significantly for large size problems.     

精神疾病患者经济负担分析及预测

对某精神疾病的专科医院患者数量及费用进行分析,采用径向基函数(RBF)神经网络模型对精神疾病患者的看病费用进行拟合及预测,并比较该预测模型与BP神经网络的预测效果.将贵州省某精神类疾病的专科医院2015年1月-2016年12月医院HIS系统中的病人处方数据作为训练集,建立BP模型、RBF神经网络模型.分别对2017年1月1日-2017年1月16日病人用以精神类疾病看病费用情况进行预测.RBF神经网络模型均能够较好地拟合和预测精神类疾病患者看病费用,可以为医院管理者了解本院精神病患者看病费用的变化趋势提供依据,为制定精神病患者疾病负担的相关政策提供数据支撑.     

基于粒子群的模糊神经网络的短时交通流量组合预测

为了进一步提高短时交通流量预测的精度,提出了一种粒子群算法的模糊神经网络组合预测模型,模糊神经网络融合了神经网络的学习机制和模糊系统的语言推理能力等优点,弥补各自不足,将自回归求和滑动平均(ARIMA)和灰色Verhulst模型进行初步预测,并将两种初步预测的结果作为模糊神经网络的输入,构建基于改进模神经网络的组合预测模型,在此基础上进行训练和预测,其中模糊神经网络的相关参数由改进粒子群来优化,利用本方法来对南京市汉中路短时交通流量进行预测,结论表明:方法充分发挥了单一模型的优势,比单一的预测模型更加精确,是短时交通流量预测的一个有效方法。     

Target training with soft computing tools

The personal capabilities and intentions of employees indicate their performance within their organization. It is important for the organization to capture this kind of tacit knowledge since the workforce are the true experts in perceiving the organization's current reality and evaluating which assets require development – including themselves as knowledge assets. The collective inner voice of the workforce helps the organization's management to steer the company and its assets in a sustainable direction.This article presents how the collective inner voice of the workforce can be captured and how it can be used for the benefit of the organization and its employees. The objective is to support individuals’ personal aspirations, as well as to save the money, time and resources that an organization spends on personnel training.The focus of this article is on demonstrating a possible soft-computing method used for competency simulation. The process starts with a linguistic self-evaluation conducted by employees, where individuals’ own perception of current and target competence levels is captured. The self-evaluation is conducted with the help of fuzzy logic. Clusters are formed from the result dataset using an unsupervised neural network clustering method: self-organizing maps. A demonstrator tool is then used to perform a “what-if” type of analysis/simulation on the clusters in the results. With the demonstrator tool, employees can roughly test the impact of alternative training scenarios for themselves. For individuals this may open up new directions for self-development, and for organizations this may allow the efficient use of training resources. We tested the approach with a dataset from a real human resource development project among nuclear power plant operators.The case study reveals the potential of soft-computing based collective competency simulation as one part of personnel development projects in the future. Yet the techniques and the demonstrator tool used in this experiment are far from being products that employees could easily use as part of their training project. Possible benefits of the proposed approach are demonstrated in this article.     

肝癌术后无瘤生存期的人工神经网络预测

本文采用BP人工神经网络方法 ,构建预测肝癌患者生存期的网络模型 ,预测肝癌患者术后无瘤生存时间。该网络的回代贡献率为 83.94 % ,预测值与实际值的相关系数为 0 .916 2 ,误差均方为 0 .1347;网络对于检验集的贡献率为 71.11% ,预测值与实际值的相关系数为 0 .84 33,误差均方为 0 .16 0 8,生存期的预测值和实际值间比较 ,经配对t检验 ,t为 0 .3977,p为 0 .6 916。故认为BP神经网络预测肝癌患者的生存期效果较好 ,可进一步推广应用     

Applications of Artificial Neural Networks for Periodic Structures Analysis

The paper presents the nondeterministic, based on artificial neural network application approach analysis of periodic structures. We can distinguish several examples where the problem may be observed: conventional and magnetic railways, high building constructions that consist of repeatable blocks, ship and aeroplane bodies, space-shuttle periodic designs, long-beam antenna structures or mistuned blade disks with friction damping elements. The scope of research is to examine possibilities of use the neural networks for mistuning parameters definition and also to denominate its possible causes. The results obtained via neural network simulator training process are compared with the calculations based on mathematical model. (© 2005 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

基于MIV-BP型网络实验的房地产项目风险识别研究

为了更准确更客观地识别房地产项目中的风险,为房地产项目投资决策提供科学依据和参考,有效地规避风险,本研究在BP神经网络 (Back-Propagation Neural Network)建模的基础上,采取MIV(Mean Impact Value)算法对BP神经网络模型进行变量筛选的网络优化和改良,从而形成新的优化后的MIV-BP(Mean Impact Value Back-Propagation Neural Network)神经网络,并以此用于评价房地产项目中的风险度以及各因素在风险度中的影响作用大小;同时选取目前相关的房地产项目数据进行仿真实证分析和验证。验证实验结果表明,MIV-BP型神经网络对于房地产项目风险度识别具有良好的适应性和准确性,实验结果客观,达到专家评价的要求,并在风险因素作用度分析上具有良好的应用价值。     

基于实物期权定价方法的林业碳汇项目投资决策仿真研究

文章针对林业碳汇项目投资决策的复杂性、动态性和不确定性过程,利用林业—碳汇共同经营决策模型计算林业碳汇项目在投资期内的期望价值,采用实物期权定价方法对不同阶段不同策略下的林业碳汇项目价值进行评估,同时提出了多主体仿真建模方法,利用NetLogo仿真软件对林业碳汇项目投资决策过程进行动态模拟。仿真系统中涉及到的主体有林地、CO₂和投资者,投资者主要是作为观察者的身份,在不同阶段会做出不同的投资策略。模拟仿真三种不同状态下投资者的决策变化:一是传统林业投资动态模拟,不包含碳汇和期权因素动态模拟;二是引入碳汇市场后的林业投资动态模拟;三是引入碳汇市场和期权后林业投资动态模拟。NetLogo仿真分析结果表明引入碳汇市场可以提高投资者的收益并改变投资者的经营策略,同时引入期权,不仅增加了投资者的积极性而进行扩张投资,还可以更好地发挥林木碳汇功能,体现林业的生态价值及经济价值。     

基于灰色关联分析的BP神经网络模型在中国碳排放预测中的应用

利用灰色关联分析原理,对中国碳排放影响因素进行筛选,再利用BP神经网络模型对中国碳排放进行预测,从而大大地提高了神经网络的训练速度,并且达到了良好的预测效果,从而为中国碳排放预测提供了新的科学工具.     

Representing and Learning Distributions with the Aid of a Neural Network

This paper describes a method by which a neural network learns to fit a distribution to sample data. The neural network may be used to replace the input distributions required in a simulation or mathematical model and it allows random variates to be generated for subsequent use in the model. Results are given for several data sets which indicate the method is robust and can represent different families of continuous distributions. The neural network is a three-layer feed-forward network of size (1-3-3-1). This paper suggests that the method is an alternative approach to the problem of selection of suitable continuous distributions and random variate generation techniques for use in simulation and mathematical models.     

A locally linear neuro-fuzzy model for supplier selection in cosmetics industry

Supplier selection and evaluation is a complicated and disputed issue in supply chain network management, by virtue of the variety of intellectual property of the suppliers, the several variables involved in supply demand relationship, the complex interactions and the inadequate information of suppliers. The recent literature confirms that neural networks achieve better performance than conventional methods in this area. Hence, in this paper, an effective artificial intelligence (AI) approach is presented to improve the decision making for a supply chain which is successfully utilized for long-term prediction of the performance data in cosmetics industry. A computationally efficient model known as locally linear neuro-fuzzy (LLNF) is introduced to predict the performance rating of suppliers. The proposed model is trained by a locally linear model tree (LOLIMOT) learning algorithm. To demonstrate the performance of the proposed model, three intelligent techniques, multi-layer perceptron (MLP) neural network, radial basis function (RBF) neural network and least square-support vector machine (LS-SVM) are considered. Their results are compared by using an available dataset in cosmetics industry. The computational results show that the presented model performs better than three foregoing techniques.     

The use of random weights for the training of multilayer networks of neurons with Heaviside characteristics

Artificial neural networks have, in recent years, been very successfully applied in a wide range of areas. A major reason for this success has been the existence of a training algorithm called backpropagation. This algorithm relies upon the neural units in a network having input/output characteristics that are continuously differentiable. Such units are significantly less easy to implement in silicon than are neural units with Heaviside (step-function) characteristics. In this paper, we show how a training algorithm similar to backpropagation can be developed for 2-layer networks of Heaviside units by treating the network weights (i.e., interconnection strengths) as random variables. This is then used as a basis for the development of a training algorithm for networks with any number of layers by drawing upon the idea of internal representations. Some examples are given to illustrate the performance of these learning algorithms.     

大额资产清仓的自动化交易策略

市场的机构投资者经常需要清仓手中持有的大额资产, 因此清仓的交易策略成为了关心的问题. 以工商银行的股票为例,给出适用于计算机执行的自动化清仓策略. 首先将高频的工商银行股票历史数据在每个交易日分别划分出48个交易期, 将问题简化为处理每个交易日交易期的数据. 在此基础上, 综合考虑用神经网络模拟预测清仓时股票价格随时间下降的风险和用信息流理论模型衡量的价格冲击和交易时刻, 并通过优化模型得到清仓持续的交易日天数. 此后, 再制定出每个交易日的具体自动化交易策略.在制定日内交易策略 时, 首先用神经网络对交易时刻做出预测, 然后综合考虑使用 VWAP 预测出的交易量和通过 Kalman 滤波方法修正过的期权定价公式预测出的各时刻股票的初始价格, 最终给出详细的交易策略及交易的成本.     

Development and Evaluation of Decision-Making Model for Stock Markets

The paper introduces an intelligent decision-making model which is based on the application of artificial neural networks (ANN) and swarm intelligence technologies. The proposed model is used to generate one-step forward investment decisions for stock markets. The ANN are used to make the analysis of daily stock returns and to calculate one day forward decision for purchase of the stocks. Subsequently the Particle Swarm Optimization (PSO) algorithm is applied in order to select the “the best” ANN for the future investment decisions and to adapt the weights of other networks towards the weights of the best network. The experimental investigations were made considering different forms of decision-making model: different number of ANN, ANN inputs, sliding windows, and commission fees. The paper introduces the decision-making model, its evaluation results and discusses its application possibilities.     

Neuro-fuzzy technique for navigation of multiple mobile robots

In this paper, navigation techniques for several mobile robots are investigated in a totally unknown environment. In the beginning, Fuzzy logic controllers (FLC) using different membership functions are developed and used to navigate mobile robots. First a fuzzy controller has been used with four types of input members, two types of output members and three parameters each. Next two types of fuzzy controllers have been developed having same input members and output members with five parameters each. Each robot has an array of sensors for measuring the distances of obstacles around it and an image sensor for detecting the bearing of the target. It is found that the FLC having Gaussian membership function is best suitable for navigation of multiple mobile robots. Then a hybrid neuro-fuzzy technique has been designed for the same problem. The neuro-fuzzy technique being used here comprises a neural network, which is acting as a pre processor for a fuzzy controller. The neural network considered for neuro-fuzzy technique is a multi-layer perceptron, with two hidden layers. These techniques have been demonstrated in simulation mode, which depicts that the robots are able to avoid obstacles and reach the targets efficiently. Amongst the techniques developed neuro-fuzzy technique is found to be most efficient for mobile robots navigation. Experimental verifications have been done with the simulation results to prove the authenticity of the developed neuro-fuzzy technique.     

基于BP神经网络的行业间中小企业信用评估模型及应用

首先构建了行业间中小企业信用评估指标体系,然后利用安徽省不同行业的800家中小企业调查数据,将其分为训练样本集和测试样本集,对BP神经网络的构造进行讨论,确定BP神经网络的算法,建立起基于BP神经网络的行业间信用评估模型,并代入2003年度全国农业和工业的部分分行业数据进行实证,并对仿真结果做出分析,指出造成农、工行业信用较大差距的原因,并提出加强农业行业信用建设的建议.