Adaptive Neurofuzzy Computing Technique for Evapotranspiration Estimation

The accuracy of an adaptive neurofuzzy computing technique in estimation of reference evapotranspiration (ET0) is investigated in this paper. The daily climatic data, solar radiation, air temperature, relative humidity, and wind speed from two stations, Pomona and Santa Monica, in Los Angeles, Calif., are used as inputs to the neurofuzzy model to estimate ET0 obtained using the FAO-56 Penman–Monteith equation. In the first part of the study, a comparison is made between the estimates provided by the neurofuzzy model and those of the following empirical models: The California Irrigation Management System, Penman, Hargreaves, and Ritchie. In this part of the study, the empirical models are calibrated using the standard FAO-56 PM ET0 values. The estimates of the neurofuzzy technique are also compared with those of the calibrated empirical models and artificial neural network (ANN) technique. Mean-squared errors, mean-absolute errors, and determination coefficient statistics are used as comparing criteria for the evaluation of the models’ performances. The comparison results reveal that the neurofuzzy models could be employed successfully in modeling the ET0 process. In the second part of the study, the potential of the neurofuzzy technique, ANN and the empirical methods in estimation ET0 using nearby station data are investigated.     

Prediction of Engineering Performance: A Neurofuzzy Approach

Engineering and design professionals constitute a major driving force for a successful project undertaking. Although the industry has been active in addressing the performance of construction labor and methods to estimate or predict such performance, relatively fewer efforts have been conducted for the engineering profession. In an attempt to fill out this gap, the paper presents a study to utilize neurofuzzy intelligent systems for predicting the engineering performance in a construction project. First, neurofuzzy systems are introduced as integrated schemes of artificial neural networks and fuzzy control systems. The use of these neurofuzzy intelligent systems, particularly fuzzy neural networks, in predicting engineering performance is then demonstrated in the industrial construction sector. The development of the system is based on actual project data that was collected through questionnaire surveys. Statistical variable reduction techniques are further employed to develop linear regression models of the same engineering performance prediction scheme, and results are being compared between both techniques.     

Comparison of Adaptive Network Based Fuzzy Inference Systems and B-spline Neuro-Fuzzy Mode Choice Models

This paper investigates the use of neuro-fuzzy models for behavioral mode choice modeling. The concept of neuro-fuzzy models has emerged in recent years as researchers have tried to combine the transparent, linguistic representation of a fuzzy system with the learning ability of artificial neural networks. Several neuro-fuzzy systems have been reported in the literature. They include various representations and architectures and therefore are suitable for different applications. In this paper, the performance of two of the most widely used neuro-fuzzy models, namely: B-spline associative memory networks and adaptive network based fuzzy inference systems, is compared. The theoretical backgrounds of both systems are presented and their relative advantages are discussed using a mode choice modeling case study. Areas of comparison include: model performance, dealing with the curse of dimensionality, automatic exclusion of irrelevant inputs, and model transparency.     

Cloud Neural Fuzzy PID Hybrid Integrated Algorithm of Flatness Control

In connection with the characteristics of multi-disturbance and nonlinearity of a system for flatness control in cold rolling process, a new intelligent PID control algorithm was proposed based on a cloud model, neural network and fuzzy integration. By indeterminacy artificial intelligence, the problem of fixing the membership functions of input variables and fuzzy rules was solved in an actual fuzzy system and the nonlinear mapping between variables was implemented by neural network. The algorithm has the adaptive learning ability of neural network and the indetermi- nacy of a cloud model in processing knowledge, which makes the fuzzy system have more persuasion in the process of knowledge inference, realizing the online adaptive regulation of PID parameters and avoiding the defects of the traditional PID controller. Simulation results show that the algorithm is simple, fast and robust with good control performance and application value.     

Prediction of Sediment Load Concentration in Rivers using Artificial Neural Network Model

An artificial neural model is used to estimate the natural sediment discharge in rivers in terms of sediment concentration. This is achieved by training the network to extrapolate several natural streams data collected from reliable sources. The selection of water and sediment variables used in the model is based on the prior knowledge of the conventional analyses, based on the dynamic laws of flow and sediment. Choosing an appropriate neural network structure and providing field data to that network for training purpose are addressed by using a constructive back-propagation algorithm. The model parameters, as well as fluvial variables, are extensively investigated in order to get the most accurate results. In verification, the estimated sediment concentration values agree well with the measured ones. The model is evaluated by applying it to other groups of data from different rivers. In general, the new approach gives better results compared to several commonly used formulas of sediment discharge.     

Segmentation of multispectral magnetic resonance image using penalized fuzzy competitive learning network

Segmentation (tissue classification) of the medical images obtained from Magnetic resonance (MR) images is a primary step in most applications of computer vision to medical image analysis. This paper describes a penalized fuzzy competitive learning network designed to segment multispectral MR spin echo images. The proposed approach is a new unsupervised and winner-takes-all scheme based on a neural network using the penalized fuzzy clustering technique. Its implementation consists of the combination of a competitive learning network and penalized fuzzy clustering methods in order to make parallel implementation feasible. The penalized fuzzy competitive learning network could provide an acceptable result for medical image segmentation in parallel processing using the hardware implementation. The experimental results show that a promising solution can be obtained using the penalized fuzzy competitive learning neural network based on least squares criteria.     

Overview of the Application of “Fuzzy Techniques” in Construction Management Research

During the last decade, “fuzzy techniques” have been increasingly applied to the research area of construction management discipline. To date, however, no paper has attempted to summarize and present a critique of the existing “fuzzy” literature. This paper, therefore, aims to comprehensively review the fuzzy literature that has been published in eight selected top quality journals from 1996 to 2005, these being Journal of Construction Engineering and Management, ASCE; Journal of Management in Engineering, ASCE; Construction Management and Economics; Engineering, Construction and Architectural Management; International Journal of Project Management; Building Research and Information; Building and Environment; and Benchmarking: An International Journal. It has been found that fuzzy research, as applied in construction management discipline in the past decade, can be divided into two broad fields, encompassing: (1) fuzzy set/fuzzy logic; and (2) hybrid fuzzy techniques, with the applications in four main categories, including: (1) decision making; (2) performance; (3) evaluation/assessment; and (4) modeling. The comprehensive review provided in this paper offers new directions for fuzzy research and its application in construction management. Based on a comprehensive literature review on the applications of fuzzy set/fuzzy logic, and hybrid fuzzy techniques in construction management research, an increasing trend of applying these techniques in construction management research is observed. Therefore, it is suggested that future research studies related to fuzzy techniques can be continuously applied to these four major categories. Fuzzy membership functions and linguistic variables in particular can be used to suit applications to solving problems encountered in the construction industry based on the nature of construction, which are widely regarded as complicated, full of uncertainties, and contingent on changing environments. Moreover, hybrid fuzzy techniques, such as neurofuzzy and fuzzy neural networks, can be more widely applied because they can better tackle some problems in construction that fuzzy set/fuzzy logic alone may not best suit. For example, neural networks are strong in pattern recognition and automatic learning while fuzzy set and fuzzy logic are strong in modeling certain uncertainties. Their combination can assist in developing models with uncertainty under some forms of pattern. Finally, an increasing trend of applying fuzzy techniques in the building science and environmental disciplines is also observed; it is believed that the application of fuzzy techniques will go beyond the construction management area into these disciplines as well.     

Preliminary Design System for Concrete Box Girder Bridges

A neural network-based design system is presented in this paper for preliminary design of concrete box girder bridges. The system is based on a loose coupling model that integrates the artificial neural network and the fuzzy network to perform the task of noisy data filtering, knowledge extraction, and candidate synthesis. After a comparative study, the radial basis function neural network is chosen in the design knowledge generation instead of the commonly used back-propagation neural network. The fuzzy network is employed to determine the integer types of design parameters. The developed system provides a few feasible design configurations, and enables the user to overwrite some of the design parameters, so that that user can have a wide choice in his preliminary design. The accuracy of the neural network testing and the influence of the size of the design cases on the neural network prediction are discussed. A design example is included to illustrate the design procedure.     

Data Division for Developing Neural Networks Applied to Geotechnical Engineering

In recent years, artificial neural networks (ANNs) have been applied to many geotechnical engineering problems with some degree of success. In the majority of these applications, data division is carried out on an arbitrary basis. However, the way the data are divided can have a significant effect on model performance. In this paper, the issue of data division and its impact on ANN model performance is investigated for a case study of predicting the settlement of shallow foundations on granular soils. Four data division methods are investigated: (1) random data division; (2) data division to ensure statistical consistency of the subsets needed for ANN model development; (3) data division using self-organizing maps (SOMs); and (4) a new data division method using fuzzy clustering. The results indicate that the statistical properties of the data in the training, testing, and validation sets need to be taken into account to ensure that optimal model performance is achieved. It is also apparent from the results that the SOM and fuzzy clustering methods are suitable approaches for data division.     

Estimating Labor Production Rates for Industrial Construction Activities

This paper discusses an approach based on artificial neural networks that enables an estimator to produce accurate labor production rates (labor∕unit) for industrial construction tasks such as welding and pipe installation. The paper first reviews factors that were found to affect labor production rates on industrial construction tasks, current estimating practices and their limitations, and the process followed in collecting historical production rates. An artificial neural network model is then described. The model is composed of a two-stage artificial neural network, which is used to predict an efficiency multiplier (an index) based on input factors identified by the user. The multiplier is then used to adjust an average production rate given in man-hours∕unit for use on a specific project. Estimates of production rates from the new approach are compared to the existing estimating practices and conclusions are presented.     

基于模糊聚类的模糊神经网络的系统辨识

本文采用基于模糊聚类的模糊神经网络模型对系统进行辨识,首先利用模糊聚类技术来确定系统的模糊空间和模糊规则数,然后利用模糊神经网络来调整模型的前件参数和后件参数。用此设计方法对函数逼近问题进行仿真,结果表明利用聚类技术可以获得较好的初始值,学习速度快、建模精度高。     

New Approach to Designing Multilayer Feedforward Neural Network Architecture for Modeling Nonlinear Restoring Forces. II: Applications

Based on the basic formulation developed in a companion paper, the writers now present the application of an artificial neural network approach to designing streamlined network models to simulate and identify the nonlinear dynamic response of single-degree-of-freedom oscillators using the restoring force-state mapping interpretation. The neural networks which use sigmoidal activation functions are shown to be highly robust in modeling a wide variety of commonly observed nonlinear structural dynamic response behaviors. By streamlining the networks, individual network model parameters take on physically or geometrically interpretable meaning, and hence, the network initialization can be achieved through an engineered approach rather than through less physically meaningful numerical initialization schemes. Although not proven in general, examples show that by starting with a more meaningful initial design, identification convergence is improved, and the final identified model parameters are seen to have a more physical meaning. A set of model architecture prototypes is developed to capture commonly observed nonlinear response behaviors.     

Neurofuzzy Decision Support System for Efficient Risk Allocation in Public-Private Partnership Infrastructure Projects

The performance of public-private partnership (PPP) infrastructure projects is largely contingent on whether the adopted risk allocation (RA) strategy is efficient. Theoretical frameworks drawing on the transaction cost economics and the resource-based view of organizational capability are able to explain the underlying mechanism but unable to accurately forecast efficient RA strategies. In this paper, a neurofuzzy decision support system (NFDSS) was developed to assist in the RA decision-making process in PPP projects. By combining fuzzy and neural network techniques, a synthesized fuzzy inference system was established and taken as the core component of the NFDSS. Evaluation results show that the NFDSS can forecast efficient RA strategies for PPP infrastructure projects at a highly accurate and effective level. A real PPP infrastructure project is used to demonstrate the NFDSS and its practical significance.     

ANN and Fuzzy Logic Models for Simulating Event-Based Rainfall-Runoff

This study presents the development of artificial neural network (ANN) and fuzzy logic (FL) models for predicting event-based rainfall runoff and tests these models against the kinematic wave approximation (KWA). A three-layer feed-forward ANN was developed using the sigmoid function and the backpropagation algorithm. The FL model was developed employing the triangular fuzzy membership functions for the input and output variables. The fuzzy rules were inferred from the measured data. The measured event based rainfall-runoff peak discharge data from laboratory flume and experimental plots were satisfactorily predicted by the ANN, FL, and KWA models. Similarly, all the three models satisfactorily simulated event-based rainfall-runoff hydrographs from experimental plots with comparable error measures. ANN and FL models also satisfactorily simulated a measured hydrograph from a small watershed 8.44?km2 in area. The results provide insights into the adequacy of ANN and FL methods as well as their competitiveness against the KWA for simulating event-based rainfall-runoff processes.     

A New Flatness Pattern Recognition Model Based on Cerebellar Model Articulation Controllers Network

In the traditional flatness pattern recognition neural network, the topologic configurations need to be rebuilt with a changing width of cold strip. Furthermore, the large learning assignment, slow convergence, and local minimum in the network are observed. Moreover, going by the structure of the traditional neural network, according to experience, the model is time-consuming and complex. Thus, a new approach of flatness pattern recognition is proposed based on the CMAC （cerebellar model articulation controllers） neural network. The difference in fuzzy distances between samples and the basic patterns is introduced as the input of the CMAC network. Simultaneously, the adequate learning rate is improved in the error correction algorithm of this neural network. The new approach with advantages, such as high learning speed, good generalization, and easy implementation, is efficient and intelligent. The simulation results show that the speed and accuracy of the flatness pattern recognition model are obviously im proved.     

Neural Network Model for Estimating Construction Productivity

The paper describes an approach developed to estimate construction productivity for concrete formwork tasks. The system utilizes artificial neural networks, historical information, and input from experienced superintendents employed by a leading construction general contractor. It also summarizes a study undertaken to determine factors that affect labor productivity, the survey conducted to collect relevant data, and the design, training, and implementation of artificial neural networks at the participating company. A number of alternative neural network structures were investigated, the adopted one was a three-layered network with a fuzzy output structure. It was found that this structure provided the most suitable model since much of the input was subjective. A brief overview of the computer implementations and the overall experience with the system development is also provided. The method was compared to an existing statistical model developed by the same contractor and was found to improve the quality of the estimates attained. A case study conducted in the context of a workshop with senior estimators is also presented.     

A medical expert system approach using artificial neural networks for standardized treatment planning

PURPOSE: Many radiotherapy treatment plans involve some level of standardization (e.g., in terms of beam ballistics, collimator settings, and wedge angles), which is determined primarily by tumor site and stage. If patient-to-patient variations in the size and shape of relevant anatomical structures for a given treatment site are adequately sampled, then it would seem possible to develop a general method for automatically mapping individual patient anatomy to a corresponding set of treatment variables. A medical expert system approach to standardized treatment planning was developed that should lead to improved planning efficiency and consistency. METHODS AND MATERIALS: The expert system was designed to specify treatment variables for new patients based upon a set of templates (a database of treatment plans for previous patients) and a similarity metric for determining the goodness of fit between the relevant anatomy of new patients and patients in the database. A set of artificial neural networks was used to optimize the treatment variables to the individual patient. A simplified example, a four-field box technique for prostate treatments based upon a single external contour, was used to test the viability of the approach. RESULTS: For a group of new prostate patients, treatment variables specified by the expert system were compared to treatment variables chosen by the dosimetrists. Performance criteria included dose uniformity within the target region and dose to surrounding critical organs. For this standardized prostate technique, a database consisting of approximately 75 patient records was required for the expert system performance to approach that of the dosimetrists. CONCLUSIONS: An expert system approach to standardized treatment planning has the potential of improving the overall efficiency of the planning process by reducing the number of iterations required to generate an optimized dose distribution, and to function most effectively, should be closely integrated with a dosimetric based treatment planning system.     

镍闪速熔炼过程的模糊建模

针对冶金工业中镍闪速熔炼复杂工艺过程,提出了利用模糊理论建立镍闪模型的方法。一种方法是利用专家知识和操作经验（即ＩＦ－ＴＨＥＮ规则）建立闪速炉的先验模型;另一种方法是利用自适应模糊神经网络方法建立闪速炉的学习模型。综合考虑两种模型的建模结果,建立整个的模型。经过两个月的现场离线指导表明,这种建模方法能够较为准确地反映镍闪速炉的运行过程。     

ANFIS模型在围岩稳定性分类中的应用

将模糊理论和人工神经网络理论相结合,建立了一种自适应神经模糊推理系统(ANFIS),应用于地下工程围岩稳定性分类.并根据收集到的围岩分类资料作为样本来训练和测试网络模型.预测结果表明,该模型能较好地用于地下硐室围岩分类.     

Estimating Actual Evapotranspiration from Limited Climatic Data Using Neural Computing Technique

This paper examines the potential of artificial neural networks (ANN) in estimating the actual crop evapotranspiration (ET) from limited climatic data. The study employed radial-basis function (RBF) type ANN for computing the daily values of ET for rice crop. Six RBF networks, each using varied input combinations of climatic variables, have been trained and tested. The model estimates are compared with measured lysimeter ET. The results of the study clearly demonstrate the proficiency of the ANN method in estimating the ET. The analyses suggest that the crop ET could be computed from air temperature using the ANN approach. However, the present study used a single crop data for a limited period, therefore further studies using more crops as well as weather conditions may be required to strengthen these conclusions.