A committee machine with empirical formulas for permeability prediction

This study integrates log-derived empirical formulas and the concept of the committee machine to develop an improved model for predicting permeability. A set of three empirical formulas, such as the Wyllie–Rose, Coates–Dumanoir, and porosity models to correlate reservoir well-logging information with measured core permeability, are used as expert members in a committee machine. A committee machine, a new type of neural network, has a parallel architecture that fuses knowledge by combining the individual outputs of its experts to arrive at an overall output. In this study, an ensemble-based committee machine with empirical formulas (CMEF) is used. This machine combines three individual formulas, each of which performs the same evaluation task. The overall output of each ensemble member is then computed according to the coefficients (weights) of the ensemble averaging method that reflects the contribution of each formula. The optimal combination of weights for prediction is also investigated using a genetic algorithm.We illustrate the method using a case study. Eighty-two data sets composed of well log data and core data were clustered into 41 training sets to construct the model and 41 testing sets to validate the model's predictive ability. A comparison of prediction results from the CMEF model and from three individual empirical formulas showed that the proposed CMEF model for permeability prediction provided the best generalization and performance for validation. This indicated that the CMEF model was more accurate than any one of the individual empirical formulas performing alone.     

Petrophysical data prediction from seismic attributes using committee fuzzy inference system

This study presents an intelligent model based on fuzzy systems for making a quantitative formulation between seismic attributes and petrophysical data. The proposed methodology comprises two major steps. Firstly, the petrophysical data, including water saturation (S_w) and porosity, are predicted from seismic attributes using various fuzzy inference systems (FISs), including Sugeno (SFIS), Mamdani (MFIS) and Larsen (LFIS). Secondly, a committee fuzzy inference system (CFIS) is constructed using a hybrid genetic algorithms-pattern search (GA-PS) technique. The inputs of the CFIS model are the outputs and averages of the FIS petrophysical data. The methodology is illustrated using 3D seismic and petrophysical data of 11 wells of an Iranian offshore oil field in the Persian Gulf. The performance of the CFIS model is compared with a probabilistic neural network (PNN). The results show that the CFIS method performed better than neural network, the best individual fuzzy model and a simple averaging method.     

A neural-network committee machine approach to the inverse kinematics problem solution of robotic manipulators

In robotics, inverse kinematics problem solution is a fundamental problem in robotics. Many traditional inverse kinematics problem solutions, such as the geometric, iterative, and algebraic approaches, are inadequate for redundant robots. Recently, much attention has been focused on a neural-network-based inverse kinematics problem solution in robotics. However, the result obtained from the neural network requires to be improved for some sensitive tasks. In this paper, a neural-network committee machine (NNCM) was designed to solve the inverse kinematics of a 6-DOF redundant robotic manipulator to improve the precision of the solution. Ten neural networks (NN) were designed to obtain a committee machine to solve the inverse kinematics problem using separately prepared data set since a neural network can give better result than other ones. The data sets for the neural-network training were prepared using prepared simulation software including robot kinematics model. The solution of each neural network was evaluated using direct kinematics equation of the robot to select the best one. As a result, the committee machine implementation increased the performance of the learning.     

Hybrid fuzzy logic committee neural networks for recognition of swallow acceleration signals

Biological signals are complex and often require intelligent systems for recognition of characteristic signals. In order to improve the reliability of the recognition or automated diagnostic systems, hybrid fuzzy logic committee neural networks were developed and the system was used for recognition of swallow acceleration signals from artifacts. Two sets of fuzzy logic-committee networks (FCN) each consisting of seven member networks were developed, trained and evaluated. The FCN-I was used to recognize dysphagic swallow from artifacts, and the second committee FCN-II was used to recognize normal swallow from artifacts. Several networks were trained and the best seven were recruited into each committee. Acceleration signals from the throat were bandpass filtered, and several parameters were extracted and fed to the fuzzy logic block of either FCN-I or FCN-II. The fuzzified membership values were fed to the committee of neural networks which provided the signal classification. A majority opinion of the member networks was used to arrive at the final decision. Evaluation results revealed that FCN correctly identified 16 out of 16 artifacts and 31 out of 33 dysphagic swallows. In two cases, the decision was ambiguous due to the lack of a majority opinion. FCN-II correctly identified 24 out of 24 normal swallows, and 28 out of 29 artifacts. In one case, the decision was ambiguous due to the lack of a majority opinion. The present hybrid intelligent system consisting of fuzzy logic and committee networks provides a reliable tool for recognition and classification of acceleration signals due to swallowing.     

An application of adaptive neuro fuzzy inference system for estimating the uniaxial compressive strength of certain granitic rocks from their mineral contents

The uniaxial compressive strength (UCS) of rocks is an important intact rock parameter, and it is commonly used for various engineering applications. This parameter is mainly controlled by the mineralogical and textural characteristics of rocks. In this study, a soft computing method, an adaptive neuro-fuzzy inference system (ANFIS), was employed to estimate UCS from the mineral contents of certain granitic rocks selected from Turkey; nonlinear multiple regression analysis was then employed to validate these estimations. Five nonlinear multiple regressions and ANFIS models were constructed with three inputs: quartz, orthoclase and plagioclase. To determine the optimal model, various performance indices (R, values account for and root mean square error) were determined, and the model obtained from dataset #3 was selected as the optimal model. The coefficients of correlation for the nonlinear multiple regression and ANFIS models were 0.87 and 0.91, respectively. Thus, both models yielded acceptable results, and the ANFIS is a suitable method for estimating the UCS of rocks.     

Requirements for intelligent network service operations

A service operation architecture and operation system platform are proposed that separate commonly used information from operations functions, and that use access control functions. This enables new applications to be developed more easily and increases operating efficiency. The operation system platform is related to several surrounding platforms, and requires standardized reference points such as CMIS/P and managed objects. A managed object methodology is a suitable approach for accessing the operation system platform, and managed object classes and methods are proposed for intelligent network service operations. This architecture and platform will allow telecommunication to meet the demands created by intelligent networks for enhanced customer services, more reliable operation systems, and lower development costs. On the basis of proposed platform, service surveillance prototype systems for free-phone services have already been developed, and the next versions of the service operations systems for virtual private networks services are being developed.     

磨矿分级过程动态优化控制

建立了基于改进粒级质量平衡模型（PBM）的质量指标预测模型和保证过程最优运行的优化计算模型,提出了基于反馈校正的动态优化控制方案.首先由优化模型计算最优控制律,为消除过程扰动及其他不确定因素影响,引入质量指标反馈调节机制;然后智能控制单元根据人工测试和期望质量指标间的偏差对最优控制律进行反馈修正.现场实验结果表明,该方案能够稳定过程产品质量,实现过程节能降耗.     

Modelling of diesel engine performance using advanced machine learning methods under scarce and exponential data set

Traditional methods on creating diesel engine models include the analytical methods like multi-zone models and the intelligent based models like artificial neural network (ANN) based models. However, those analytical models require excessive assumptions while those ANN models have many drawbacks such as the tendency to overfitting and the difficulties to determine the optimal network structure. In this paper, several emerging advanced machine learning techniques, including least squares support vector machine (LS-SVM), relevance vector machine (RVM), basic extreme learning machine (ELM) and kernel based ELM, are newly applied to the modelling of diesel engine performance. Experiments were carried out to collect sample data for model training and verification. Limited by the experiment conditions, only 24 sample data sets were acquired, resulting in data scarcity. Six-fold cross-validation is therefore adopted to address this issue. Some of the sample data are also found to suffer from the problem of data exponentiality, where the engine performance output grows up exponentially along the engine speed and engine torque. This seriously deteriorates the prediction accuracy. Thus, logarithmic transformation of dependent variables is utilized to pre-process the data. Besides, a hybrid of leave-one-out cross-validation and Bayesian inference is, for the first time, proposed for the selection of hyperparameters of kernel based ELM. A comparison among the advanced machine learning techniques, along with two traditional types of ANN models, namely back propagation neural network (BPNN) and radial basis function neural network (RBFNN), is conducted. The model evaluation is made based on the time complexity, space complexity, and prediction accuracy. The evaluation results show that kernel based ELM with the logarithmic transformation and hybrid inference is far better than basic ELM, LS-SVM, RVM, BPNN and RBFNN, in terms of prediction accuracy and training time.     

ABE: an environment for engineering intelligent systems

The ABE multilevel architecture for developing intelligent systems addresses the key problems of intelligent systems engineering: large-scale applications and the reuse and integration of software components. ABE defines a virtual machine for module-oriented programming and a cooperative operating system that provides access to the capabilities of that virtual machine. On top of the virtual machine, ABE provides a number of system design and development frameworks, which embody such programming metaphors as control flow, blackboards, and dataflow. These frameworks support the construction of capabilities, including knowledge processing tools, which span a range from primitive modules to skeletal systems. Finally, applications can be built on skeletal systems. In addition, ABE supports the importation of existing software, including both conventional and knowledge processing tools     

Intelligent simulation tools for mining large scientific data sets

This paper describes problems, challenges, and opportunities forintelligent simulation of physical systems. Prototype intelligent simulation tools have been constructed for interpreting massive data sets from physical fields and for designing engineering systems. We identify the characteristics of intelligent simulation and describe several concrete application examples. These applications, which include weather data interpretation, distributed control optimization, and spatio-temporal diffusion-reaction pattern analysis, demonstrate that intelligent simulation tools are indispensable for the rapid prototyping of application programs in many challenging scientific and engineering domains.     

基于SVM的车型识别系统的设计与实现

支持向量机(SVM)理论建立在结构风险最小化原理基础上,对非线性、高维数的小样本问题有非常好的分类效果和学习推广能力。本文设计了基于支持向量机的车型识别系统,系统通过对摄像机采集的视频图像进行运动目标检测分割、特征提取与选择、模式识别等处理,达到实时车型识别。试验结果表明,该系统有很高的识别率和适应性。     

A Bayesian committee machine

The Bayesian committee machine (BCM) is a novel approach to combining estimators that were trained on different data sets. Although the BCM can be applied to the combination of any kind of estimators, the main foci are gaussian process regression and related systems such as regularization networks and smoothing splines for which the degrees of freedom increase with the number of training data. Somewhat surprisingly, we find that the performance of the BCM improves if several test points are queried at the same time and is optimal if the number of test points is at least as large as the degrees of freedom of the estimator. The BCM also provides a new solution for on-line learning with potential applications to data mining. We apply the BCM to systems with fixed basis functions and discuss its relationship to gaussian process regression. Finally, we show how the ideas behind the BCM can be applied in a non-Bayesian setting to extend the input-dependent combination of estimators.     

Evaluation and prediction of flyrock resulting from blasting operations using empirical and computational methods

Mines, quarries and construction sites face environmental impacts, such as flyrock, due to blasting operations. Flyrock may cause damage to structures and injury to human. Therefore, flyrock prediction is required to determine safe blasting zone. In this regard, 232 blasting operations were investigated in five granite quarries, Malaysia. Blasting parameters comprising maximum charge per delay and powder factor were prepared to predict flyrock using empirical and intelligent methods. An empirical graph was proposed to predict flyrock distance for different powder factor values. In addition, using the same datasets, two intelligent systems, namely artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) were used to predict flyrock. Considering some model performance indices including coefficient of determination (R ²), value account for and root mean squared error and also using simple ranking procedure, the best flyrock prediction models were selected. It was found that the ANFIS model can predict flyrock with higher performance capacity compared to ANN predictive model. R ² values of testing datasets are 0.925 and 0.964 for ANN and ANFIS techniques, respectively, suggesting the superiority of the ANFIS technique in predicting flyrock.     

A naive Bayes probability estimation model based on self-adaptive differential evolution

In the process of learning the naive Bayes, estimating probabilities from a given set of training samples is crucial. However, when the training samples are not adequate, probability estimation method will inevitably suffer from the zero-frequency problem. To avoid this problem, Laplace-estimate and M-estimate are the two main methods used to estimate probabilities. The estimation of two important parameters m (integer variable) and p (probability variable) in these methods has a direct impact on the underlying experimental results. In this paper, we study the existing probability estimation methods and carry out a parameter Cross-test by experimentally analyzing the performance of M-estimate with different settings for the two parameters m and p. This part of experimental result shows that the optimal parameter values vary corresponding to different data sets. Motivated by these analysis results, we propose an estimation model based on self-adaptive differential evolution. Then we propose an approach to calculate the optimal m and p value for each conditional probability to avoid the zero-frequency problem. We experimentally test our approach in terms of classification accuracy using the 36 benchmark machine learning repository data sets, and compare it to a naive Bayes with Laplace-estimate and M-estimate with a variety of setting of parameters from literature and those possible optimal settings via our experimental analysis. The experimental results show that the estimation model is efficient and our proposed approach significantly outperforms the traditional probability estimation approaches especially for large data sets (large number of instances and attributes).     

An extensive evaluation of seven machine learning methods for rainfall prediction in weather derivatives

Regression problems provide some of the most challenging research opportunities in the area of machine learning, and more broadly intelligent systems, where the predictions of some target variables are critical to a specific application. Rainfall is a prime example, as it exhibits unique characteristics of high volatility and chaotic patterns that do not exist in other time series data. This work’s main impact is to show the benefit machine learning algorithms, and more broadly intelligent systems have over the current state-of-the-art techniques for rainfall prediction within rainfall derivatives. We apply and compare the predictive performance of the current state-of-the-art (Markov chain extended with rainfall prediction) and six other popular machine learning algorithms, namely: Genetic Programming, Support Vector Regression, Radial Basis Neural Networks, M5 Rules, M5 Model trees, and k-Nearest Neighbours. To assist in the extensive evaluation, we run tests using the rainfall time series across data sets for 42 cities, with very diverse climatic features. This thorough examination shows that the machine learning methods are able to outperform the current state-of-the-art. Another contribution of this work is to detect correlations between different climates and predictive accuracy. Thus, these results show the positive effect that machine learning-based intelligent systems have for predicting rainfall based on predictive accuracy and with minimal correlations existing across climates.     

Cognitive model based fashion style decision making

The decision of the style of a garment was affected by not only the physical attributes of the components of the garment but also the decision context. Lacking consideration of the decision context leads to the prediction failure of many models in intelligent fashion design systems. This paper proposed to integrate the cognitive model - Multi-alternative Decision Field Theory (MDFT) with Genetic Algorithm to tackle the context problem, specifically, the choice set when a decision was made. A unified S expression and its sign deciding mechanism was given out. Experiments showed that the proposed model gives out a better prediction of human fashion style decision than GA only model.     

Semantics,intelligent processing and services for big data

With the continuous increase of data, scaling up to unprecedented amounts, generated by Internet-based systems, Big Data has emerged as a new research field, coined as “Big Data Science”. The core of Big Data Science is the extraction of knowledge from data as a basis for intelligent services and decision making systems, however, it encompasses many research topics and investigates a variety of techniques and theories from different fields, including data mining and machine learning, information retrieval, analytics, and indexing services, massive processing and high performance computing. Altogether the aim is the development of advanced data-aware knowledge based systems.This special issue presents advances in Semantics, Intelligent Processing and Services for Big Data and their applications to a variety of domains including mobile computing, smart cities, forensics and medicine.     

Fuzzy logic-based formulation of the organizer of intelligent robotic systems

A fuzzy logic-based methodology is proposed to model the organization level of an intelligent robotic system. The user input commands to the system organizer are linguistic in nature and the primitive events-tasks from the task domain of the system are, in general, interpreted via fuzzy sets. Fuzzy relations are introduced to connect every event with a specific user input command. Approximate reasoning is accomplished via a modifier and the compositional rule of inference, whereas the application of the conjunction rule generates those fuzzy sets with elements all possible (crisp) plans. Themost possible plan among all those generated, that is optimal under an application dependent criterion, is chosen and communicated to the coordination level. Off-line feedback information from the lower levels is considered asa-priori known and is used to update all organization level information. An example demonstrates the applicability of the proposed algorithm to intelligent robotic systems.     

Optimal electric power load-dispatching

An electric power load-dispatching model was developed for Du Pont's Savannah River Plant. This model economically optimizes the distribution of load simultaneously supplied by power produced at each of the three area generation stations and the amount of power purchased from South Carolina Electric and Gas Company. An iterative, mixed-integer linear programming technique is used to find the “optimal” solution.     

区间二型模糊集和模糊系统: 综述与展望

一型模糊集可以建模单个用户的语义概念中的不确定性, 即个体内不确定性. 一型模糊系统在控制和机器学习中得到了大量成功应用. 区间二型模糊集能同时建模个体内不确定性和个体间不确定性, 因而在很多应用中显示了比一型模糊系统更好的性能, 是近年来的研究热点. 本文首先介绍了区间二型模糊集的重要概念和理论研究进展, 总结了其在决策和机器学习中的成功应用, 然后介绍了区间二型模糊系统的基本操作和理论研究进展, 并回顾了其在控制和机器学习中的典型应用. 最后, 对区间二型模糊集和模糊系统未来的研究方向进行了展望.