Integration of the grey relational analysis with genetic algorithm for software effort estimation

Accurate estimates of efforts in software development are necessary in project management practices. Project managers or domain experts usually conduct software effort estimation using their experience; hence, subjective or implicit estimates occur frequently. As most software projects have incomplete information and uncertain relations between effort drivers and the required development effort, the grey relational analysis (GRA) method has been applied in building a formal software effort estimation model for this study. The GRA in the grey system theory is a problem-solving method that is used when dealing with similarity measures of complex relations. This paper examines the potentials of the software effort estimation model by integrating a genetic algorithm (GA) to the GRA. The GA method is adopted to find the best fit of weights for each software effort driver in the similarity measures. Experimental results show that the software effort estimation using an integration of the GRA with GA method presents more precise estimates over the results using the case-based reasoning (CBR), classification and regression trees (CART), and artificial neural networks (ANN) methods.     

项目组合网络实证研究

将复杂网络理论引入到项目组合管理中,以项目为节点,以项目之间的依赖关系为边,项目的成本看作点权,项目之间的依赖强度看作边权,将项目组合抽象为一个复杂加权网络。研究了4家企业的项目组合网络,在分析项目组合特性的基础上,概括了项目组合的复杂网络行为特征。对企业项目组合网络进行综合对比分析发现,项目组合网络具有如下相似特征:①节点度分布不同于其他社会网络,倾向于幂律分布,又有偏斜泊松分布的迹象;②度相关系数负相关,有别于其他社会网络;③具有集群结构;④聚集系数很大;⑤网络直径较小;⑥平均度数小于4。     

Existence and global exponential stability of anti‐periodic solutions for delayed quaternion‐valued cellular neural networks with impulsive effects

In this paper, we consider a class of delayed quaternion‐valued cellular neural networks (DQVCNNs) with impulsive effects. By using a novel continuation theorem of coincidence degree theory, the existence of anti‐periodic solutions for DQVCNNs is obtained with or without assuming that the activation functions are bounded. Furthermore, by constructing a suitable Lyapunov function, some sufficient conditions are derived to guarantee the global exponential stability of anti‐periodic solutions for DQVCNNs. Our results are new and complementary to the known results even when DQVCNNs degenerate into real‐valued or complex‐valued neural networks. Finally, an example is given to illustrate the effectiveness of the obtained results.     

The contribution of mathematical modelling to the practice of project management

This paper looks at the contribution that mathematical modellinghas made to project management over the past 50 years, and thecontribution it is currently making and can make in the future.Project Management started with well-defined foundations posingprecise, well-defined problems. In its growing phase, modellersplayed an essential role in taking the problems defined by theproject-management world and offering solutions, from the originalPERT, through resource allocation and levelling procedures,Monte Carlo simulation models, criticality analyses and so on.Since then, however, while the project management field itselfhas tried to establish its procedures, keeping to its philosophicalstance, much of the mathematical-modelling world has continuedalong its trajectory, producing ever more complex solutionsto ever more complex models, motivated by mathematical impressivenessrather than the need to solve real-world problems. This paperoutlines much of this work, some of which does find its wayinto project-network software but much of which languishes injournals. However, over the last decade or so, Operational Researchershave begun to build models of projects that are systemic anddynamic and explain many of the behaviours of projects thatconventional decomposition models do not; and at the same time,some of the Project Management world has started to realizethe limitations of its philosophical stance and started lookingto build new theory for modern, complex, dynamic projects. Asthese two trends come together, it is essential that modellersare at the forefront of building this new theory.     

Estimations of trout density and biomass: a neural networks approach

In this paper, we report the use of artificial neural networks to predict the density and biomass of trout in the Pyrenees mountains from 8 physical parameters of the environment. The results obtained with a three-layered neural network are presented. Studies have been undertaken with 1 or 4 variables in the output layer of the network. Results on the test set (generalization of models) are satisfactory with determination coefficients R² exceeding 0.76.     

Posibilidades de un modelo sustituto de incendios mediante el empleo de redes neuronales

In recent years there has been extensive development of the fire computer models, and its use in the study of the fire safety, fire investigation, etc. has been increased. The most important types of fire computer models are the field model and the zone model. The first model reaches a better approximation to fire dynamics, but the second one requires less computational time.Additionally, in the last years, it should be noted the great advances in information processing using artificial neural networks, and it has become a useful tool with application in very diverse fields.This paper analyzes the possibilities of develop a new fire computer model using artificial neural networks. In the first approach to this objective, a simple compartment was analyzed with a field model. After that, simulations employing General Regression Neural Network were performed. This method achieves similar results that the field model employing computational times closer to the zone models. The neural network has been trained with FDS field model and validating the resulting model with data from a full scale test. In later stages other phenomena and different types of networks will be evaluated.     

Optimizing base station location and configuration in UMTS networks

Radio planning and coverage optimization are critical issues when deploying and expanding third generation cellular systems. We investigate mixed integer programming models for locating and configuring base stations in UMTS networks so as to maximize coverage and minimize installation costs. The overall model considers both uplink and downlink directions, that we studied separately in Amaldi et al. (2002, 2003b). The two-stage Tabu Search algorithm we propose exploits solutions of a simplified model for the uplink direction to drastically reduce the computational time required to find good approximate solutions of the overall uplink and downlink model.Computational results obtained for realistic instances %with voice as well as data traffic are reported and discussed. Research carried out within the national project “Optimization, simulation and complexity in the design and management of telecommunication networks”funded by the Italian Ministry of Education, University and Scientific Research (MIUR).     

Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis

In this paper, we present a general framework for understanding the role of artificial neural networks (ANNs) in bankruptcy prediction. We give a comprehensive review of neural network applications in this area and illustrate the link between neural networks and traditional Bayesian classification theory. The method of cross-validation is used to examine the between-sample variation of neural networks for bankruptcy prediction. Based on a matched sample of 220 firms, our findings indicate that neural networks are significantly better than logistic regression models in prediction as well as classification rate estimation. In addition, neural networks are robust to sampling variations in overall classification performance.     

宏观经济预测模型体系研究

针对我国宏观经济管理的实际需要,以国家和地区宏观经济中长期预测和规划为研究目的,本建立了一个以投入产出模型和人工神经网络模型为核心,结合使用最优化技术的宏观经济预测模型体系,该预测模型体系已应用于某市“十五”时期的宏观经济指标测算中,预测结果已被政府计划部门在研究制定“十五”计划时采用。     

Project dynamics and emergent complexity

This paper presents a theoretical analysis of project dynamics and emergent complexity in new product development (NPD) projects subjected to the management concept of concurrent engineering. To provide a comprehensive study, the complexity frameworks, theories and measures that have been developed in organizational theory, systematic engineering design and basic scientific research are reviewed. For the evaluation of emergent complexity in NPD projects, an information-theory quantity—termed “effective measure complexity” (EMC)—is selected from a variety of measures, because it can be derived from first principles and therefore has high construct validity. Furthermore, it can be calculated efficiently from dynamic generative models or purely from historical data, without intervening models. The EMC measures the mutual information between the infinite past and future histories of a stochastic process. According to this principle, it is particularly interesting to evaluate the time-dependent complexity in NPD and to uncover the relevant interactions. To obtain analytical results, a model-driven approach is taken and a vector autoregression (VAR) model of cooperative work is formulated. The formulated VAR model provided the foundation for the calculation of a closed-form solution of the EMC in the original state space. This solution can be used to analyze and optimize complexity based on the model’s independent parameters. Moreover, a transformation into the spectral basis is carried out to obtain more expressive solutions in matrix form. The matrix form allows identification of the surprisingly few essential parameters and calculation of two lower complexity bounds. The essential parameters include the eigenvalues of the work transformation matrix of the VAR model and the correlations between components of performance fluctuations.     

Complex joint R&D projects: From empirical evidence to managerial implications

At present, the role of joint R&D projects becomes fundamental for understanding the process of innovation. An extensive bibliography exists on the study of the organizations; however, the networks for the development of joint R&D projects themselves as a new form of organization are still a growing field of study. The aim of this article is to provide empirical evidence on joint R&D projects management. To approach this question the starting point will be that joint R&D projects are complex phenomena whose complexity derives from the heterogeneity of agents to take part, the technological process developed and from the organizational form that supports R&D projects. The empirical evidence through European R&D Programs shows the multidimensional character of the joint R&D project concept and allows analyze their different subsystems that is, technological, structural, and governance subsystem. This study not only offers a conceptual framework to help manage these projects, but also discusses practical guidelines that may be useful for its running and management. © 2009 Wiley Periodicals, Inc. Complexity, 2009     

A comparison of neural networks and linear scoring models in the credit union environment

The purpose of the present paper is to explore the ability of neural networks such as multilayer perceptrons and modular neural networks, and traditional techniques such as linear discriminant analysis and logistic regression, in building credit scoring models in the credit union environment. Also, since funding and small sample size often preclude the use of customized credit scoring models at small credit unions, we investigate the performance of generic models and compare them with customized models. Our results indicate that customized neural networks offer a very promising avenue if the measure of performance is percentage of bad loans correctly classified. However, if the measure of performance is percentage of good and bad loans correctly classified, logistic regression models are comparable to the neural networks approach. The performance of generic models was not as good as the customized models, particularly when it came to correctly classifying bad loans. Although we found significant differences in the results for the three credit unions, our modular neural network could not accommodate these differences, indicating that more innovative architectures might be necessary for building effective generic models.     

Nonessential objectives within network approaches for MCDM

In Gal and Hanne [Eur. J. Oper. Res. 119 (1999) 373] the problem of using several methods to solve a multiple criteria decision making (MCDM) problem with linear objective functions after dropping nonessential objectives is analyzed. It turned out that the solution does not need be the same when using various methods for solving the system containing the nonessential objectives or not. In this paper we consider the application of network approaches for multicriteria decision making such as neural networks and an approach for combining MCDM methods (called MCDM networks). We discuss questions of comparing the results obtained with several methods as applied to the problem with or without nonessential objectives. Especially, we argue for considering redundancies such as nonessential objectives as a native feature in complex information processing. In contrast to previous results on nonessential objectives, the current paper focuses on discrete MCDM problems which are also denoted as multiple attribute decision making (MADM).     

Neural network as a simulation metamodel in economic analysis of risky projects

An artificial neural network (ANN) model for economic analysis of risky projects is presented in this paper. Outputs of conventional simulation models are used as neural network training inputs. The neural network model is then used to predict the potential returns from an investment project having stochastic parameters. The nondeterministic aspects of the project include the initial investment, the magnitude of the rate of return, and the investment period. Backpropagation method is used in the neural network modeling. Sigmoid and hyperbolic tangent functions are used in the learning aspect of the system. Analysis of the outputs of the neural network model indicates that more predictive capability can be achieved by coupling conventional simulation with neural network approaches. The trained network was able to predict simulation output based on the input values with very good accuracy for conditions not in its training set. This allowed an analysis of the future performance of the investment project without having to run additional expensive and time-consuming simulation experiments.     

Neural network modeling of hydrological systems: A review of implementation techniques

This paper presents a review of procedural steps and implementation techniques used in the development of artificial intelligence models, generally referred to as artificial neural networks (ANNs), within the water resources domain. It focusses on identifying different areas wherein ANNs have found application thereby elucidating its advantages and disadvantages as well as various challenges encountered in its use. Results from this review provide useful insights into how the performance of ANNs can be improved and potential areas of application that are yet to be explored in hydrological modeling. Recommendations for Resource Managers

• Development of integrated and hybrid artificial intelligent tools is critical to achieving improved forecasts in hydrological modeling studies.

• Further research into comprehending the internal mechanisms of neural networks is required to obtain a practical meaning of each network component deployed to solve real‐world problems.

• More robust optimization techniques and tools like differential evolution, particle swarm optimization and deep neural nets, are yet to be fully explored in the water resources analysis, and should be given more attention to enhance neural networks aptitude for modeling complex and nonlinear hydrological processes.

     

质量文化传播与工程管理行为耦合演化的系统动力学模型研究

为了研究质量文化建设与工程管理行为以及工程绩效的耦合演化规律,对大型工程中的质量文化建设的动态特性进行了分析,运用系统动力学构建了包括质量文化传播、工作流程、人员调配以及进度压力这四个主要模块相互整合的大型工程耦合演化系统模型。系统仿真的结果表明一些管理行为因忽视了其引发的涟漪效应,导致了质量文化建设和工程建设的失败,而该模型考虑了大型工程存在的返工回路等多种动态反馈,能够帮助工程管理者理解质量文化建设存在的动态复杂性。     

Mittag-Leffler stability and adaptive impulsive synchronization of fractional order neural networks in quaternion field

Fractional order quaternion-valued neural networks are a type of fractional order neural networks for which neuron state, synaptic connection strengths, and neuron activation functions are quaternion. This paper is dealing with the Mittag-Leffler stability and adaptive impulsive synchronization of fractional order neural networks in quaternion field. The fractional order quaternion-valued neural networks are separated into four real-valued systems forming an equivalent four real-valued fractional order neural networks, which decreases the computational complexity by avoiding the noncommutativity of quaternion multiplication. Via some fractional inequality techniques and suitable Lyapunov functional, a brand new criterion is proposed first to ensure the Mittag-Leffler stability for the addressed neural networks. Besides, the combination of quaternion-valued adaptive and impulsive control is intended to realize the asymptotically synchronization between two fractional order quaternion-valued neural networks. Ultimately, two numerical simulations are provided to check the accuracy and validity of our obtained theoretical results.     

神经元网络在SAS中的实现(Ⅰ)

大多数常见的人工神经元网络—多层感知器（ＭＬＰ），无非是非线性回归以及判别模型。其训练方法通常是适合大型并行机硬件实现的梯度下降算法。对于一般的计算环境，ＳＡＳ系统中若干优化算法远比神经元网络的并行算法更有效。本文解释神经元网络是什么，描述神经元网络与统计模型的关系，介绍神经元网络在ＳＡＳ中的实现。     

On the nature of discontinuities in system dynamics modelling of disrupted projects

When complex projects result in large-cost over-runs, managers want to understand why this happened. There may be the possibility of making a claim against another party, or managers may simply wish to learn from any mistakes made. When using system dynamics (SD) models to analyse the reasons for project over-runs as a part of a litigation or organizational learning process, there is a strong focus on explaining historical aspects precisely. This leads to a need for the inclusion of discontinuous variables. This paper discusses the nature of discontinuities in SD models of project over-runs. Examples are given to demonstrate that the modelling of such discontinuities needs to be an integral part of the continuous simulation modelling process. Their inclusion helps to improve model validity and also, by forcing validity, uncover the important drivers of project behaviour. The examples given in this paper are significant drivers and suggest important learning about the behaviour of disrupted complex projects.     

Logistic evolutionary product-unit neural networks: Innovation capacity of poor Guatemalan households

A new logistic regression algorithm based on evolutionary product-unit (PU) neural networks is used in this paper to determine the assets that influence the decision of poor households with respect to the cultivation of non-traditional crops (NTC) in the Guatemalan Highlands. In order to evaluate high-order covariate interactions, PUs were considered to be independent variables in product-unit neural networks (PUNN) analysing two different models either including the initial covariates (logistic regression by the product-unit and initial covariate model) or not (logistic regression by the product-unit model). Our results were compared with those obtained using a standard logistic regression model and allow us to interpret the most relevant household assets and their complex interactions when adopting NTC, in order to aid in the design of rural policies.