An integrated fuzzy regression-data envelopment analysis algorithm for optimum oil consumption estimation with ambiguous data

This study introduces an integrated fuzzy regression (FR) data envelopment analysis (DEA) algorithm for oil consumption estimation and optimization with uncertain and ambiguous data. This is quite important as oil consumption estimations deals with several uncertainties due to social, economic factors. Furthermore, DEA is integrated with FR because there is no clear cut as to which FR approach is superior for oil consumption estimation. The standard indicators used in this paper are population, cost of crude oil, gross domestic production (GDP) and annual oil production. Fifteen popular and most cited FR models are considered in the algorithm. Each FR model has different approach and advantages. The input data is divided into train and test data. The FR models have been tuned for all their parameters according to the train data, and the best coefficients are identified. Center of Average Method for defuzzification output process is applied. For determining the rate of error of FR models estimations, the rate of defuzzified output of each model is compared with its actual rate consumption in test data. The efficiency of 15 FR models is examined by the output-oriented Data Envelopment Analysis (DEA) model without inputs by considering three types of relative error: RMSE, MAE and MAPE. The applicability and superiority of the proposed algorithm is shown for monthly oil consumption of Canada, United States, Japan and Australia from 1990 to 2005.     

A flexible ANN-GA-multivariate algorithm for assessment and optimization of machinery productivity in complex production units

This paper presents a flexible algorithm based on artificial neural networks (ANNs), genetic algorithms (GAs), and multivariate analysis for performance assessment and optimization of complex production units (CPUs) with respect to machinery productivity indicators (MPIs). Multivariate techniques include data envelopment analysis (DEA), principal component analysis (PCA) and numerical taxonomy (NT). Two case studies are considered to show the applicability of the proposed approach. In the first case, the machinery productivity indicators are categorized into four standard classes as availability, machinery stoppage, random failure and value added and production value. In the second case, the productivity of production units in terms of health, safety, environment and ergonomics indicators is evaluated. The flexible algorithm is capable of handling both linearity and complexity of data sets. Moreover, ANN and GA are efficiently applied to cover nonlinearity and complexity of CPUs. The results are also validated and verified by the internal mechanism of the algorithm. The algorithm is applied to a large set of production units to show its superiority and applicability over conventional approaches. Results show that, in the case of having non-linear data sets, ANN outperforms GA and conventional approaches. The flexible algorithm of this study may be easily extended to other units for assessment and optimization of CPUs with respect to machinery indicators.     

Optimum model for bearing capacity of concrete-steel columns with AI technology via incorporating the algorithms of IWO and ABC

In a composite column, the performance of both the concrete and steel has a considerable effect on the structural behaviour under different loading conditions. This study applies several artificial intelligence (AI) techniques to optimise the bearing capacity of concrete-filled steel tube (CFST) columns. First, the bearing capacity values of the CFST columns are estimated by an artificial neural network (ANN) technique. Using 303 datasets, the outer diameter, concrete compressive strength, tensile yield stress of the steel column, thickness of the steel cover, and length of the applied samples are considered as the model inputs. Following a series of analyses, several ANN models are developed. The ANN model with 8 neurons and 250 iterations is determined as the best model to predict the bearing capacity of the CFST columns. Subsequently, the invasive weed optimisation (IWO) technique, which is considered the most current optimisation algorithm, is developed to maximise the results of the bearing capacity by considering the selected ANN model. To highlight the ability of IWO, the artificial bee colony (ABC) algorithm is also applied. Consequently, it is found that both optimisation algorithms can design input parameters such that the maximum value of the bearing capacity can be obtained. The bearing capacity of the CFST columns from the ABC and IWO techniques indicates that IWO has a better capability of maximising the bearing. Thus, IWO can optimise similar problems with a high rate of performance.

     

A hybrid simulation-adaptive network based fuzzy inference system for improvement of electricity consumption estimation

This paper presents a hybrid adaptive network based fuzzy inference system (ANFIS), computer simulation and time series algorithm to estimate and predict electricity consumption estimation. The difficulty with electricity consumption estimation modeling approach such as time series is the reason for proposing the hybrid approach of this study. The algorithm is ideal for uncertain, ambiguous and complex estimation and forecasting. Computer simulation is developed to generate random variables for monthly electricity consumption. Various structures of ANFIS are examined and the preferred model is selected for estimation by the proposed algorithm. Finally, the preferred ANFIS and time series models are selected by Granger–Newbold test. Monthly electricity consumption in Iran from 1995 to 2005 is considered as the case of this study. The superiority of the proposed algorithm is shown by comparing its results with genetic algorithm (GA) and artificial neural network (ANN). This is the first study that uses a hybrid ANFIS computer simulation for improvement of electricity consumption estimation.     

A hybrid procedure for extracting rules of production performance in the automobile parts industry

In the manufacturing section, due to limitations of specific resources (e.g., time, people, and equipment), key determinants such as process capacity, human resources supply, and equipment availability may be in uncertain or out-of-control environments, followed by decreasing production performance. Traditionally, earlier studies of related issues of production performance usually used statistical methods for handling these problems. However, these methods become more complex when relationships in the input/output dataset are nonlinear. Furthermore, statistical techniques rely on the restrictive assumption on linear separability, multivariate normality and independence of the predictive variables; unfortunately, many of the common models of production performance violate these assumptions. To remedy these existing shortcomings, the study proposes a hybrid procedure that focuses on the opinions of experts, discretization of decision attributes, and application of well-known artificial intelligent (AI) approaches, such as decision trees (DT), artificial neural networks (ANN), and DT+ANN techniques, for objectively classifying production performance to solve real-world problems that are faced by the automobile parts industry. Two practically collected datasets are employed to verify the proposed hybrid procedure. The experimental results reveal that the proposed hybrid procedure is a good alternative to classify production performance from an intelligent manufacturing perspective objectively. Moreover, the output that is created by the DT C4.5 algorithm is a set of comprehensible and meaningful rules applied readily in knowledge-based performance-evaluating systems for manufacturing managers and HR division managers. The study findings and implications are of value to academicians and practitioners.     

Comparison of fuzzy inference system (FIS), FIS with artificial neural networks (FIS + ANN) and FIS with adaptive neuro-fuzzy inference system (FIS + ANFIS) for inventory control

Conventional inventory models mostly cope with a known demand and adequate supply, but are not realistic for many industries. In this research, the fuzzy inference system (FIS) model, FIS with artificial neural network (ANN) model and FIS with adaptive neuro-fuzzy inference system (ANFIS) model in which both supply and demand are uncertain were applied for the inventory system. For FIS model, the generated fuzzy rules were applied to draw out the fuzzy order quantity continuously. The order quantity was adjusted according to the FIS model with the evaluation algorithm for the inventory model. The output of FIS model was also used as data for FIS + ANN and FIS + ANFIS models. The FIS + ANFIS model was studied with three membership functions; trapezoidal and triangular (Trap), Gaussian and bell shape. Inventory costs of the proposed models were compared with the stochastic economic order quantity (EOQ) models based on previous data of a case study factory. The results showed that the FIS + ANFIS_Gauss model gave the best performance of total inventory cost saving by more than 75 % compared to stochastic EOQ model.     

An optimized ANN model based on genetic algorithm for predicting ripping production

Due to the environmental constraints and the limitations on blasting, ripping as a ground loosening and breaking method has become more popular in both mining and civil engineering applications. As a result, a more applicable rippability model is required to predict ripping production (Q) before conducting such tests. In this research, a hybrid genetic algorithm (GA) optimized by artificial neural network (ANN) was developed to predict ripping production results obtained from three sites in Johor state, Malaysia. It should be noted that the mentioned hybrid model was first time applied in this field. In this regard, 74 ripping tests were investigated in the studied areas and the relevant parameters were also measured. A series of GA–ANN models were conducted in order to propose a hybrid model with a higher accuracy level. To demonstrate the performance capacity of the hybrid GA–ANN model, a pre-developed ANN model was also proposed and results of predictive models were compared using several performance indices. The results revealed higher accuracy of the proposed hybrid GA–ANN model in estimating Q compared to ANN technique. As an example, root-mean-square error values of 0.092 and 0.131 for testing datasets of GA–ANN and ANN techniques, respectively, express the superiority of the newly developed model in predicting ripping production.

     

Improved estimation of electricity demand function by using of artificial neural network,principal component analysis and data envelopment analysis

Due to various seasonal and monthly changes in electricity consumption and difficulties in modeling it with the conventional methods, a novel algorithm is proposed in this paper. This study presents an approach that uses Artificial Neural Network (ANN), Principal Component Analysis (PCA), Data Envelopment Analysis (DEA) and ANOVA methods to estimate and predict electricity demand for seasonal and monthly changes in electricity consumption. Pre-processing and post-processing techniques in the data mining field are used in the present study. We analyze the impact of the data pre-processing and post-processing on the ANN performance and a 680 ANN-MLP is constructed for this purpose. DEA is used to compare the constructed ANN models as well as ANN learning algorithm performance. The average, minimum, maximum and standard deviation of mean absolute percentage error (MAPE) of each constructed ANN are used as the DEA inputs. The DEA helps the user to use an appropriate ANN model as an acceptable forecasting tool. In the other words, various error calculation methods are used to find a robust ANN learning algorithm. Moreover, PCA is used as an input selection method, and a preferred time series model is chosen from the linear (ARIMA) and nonlinear models. After selecting the preferred ARIMA model, the Mcleod–Li test is applied to determine the nonlinearity condition. Once the nonlinearity condition is satisfied, the preferred nonlinear model is selected and compared with the preferred ARIMA model, and the best time series model is selected. Then, a new algorithm is developed for the time series estimation; in each case an ANN or conventional time series model is selected for the estimation and prediction. To show the applicability and superiority of the proposed ANN-PCA-DEA-ANOVA algorithm, the data regarding the Iranian electricity consumption from April 1992 to February 2004 are used. The results show that the proposed algorithm provides an accurate solution for the problem of estimating electricity consumption.     

The effect of ICA and PSO on ANN results in approximating elasticity modulus of rock material

Reliable determination/evaluation of the rock deformation can be useful prior any structural design application. Young’s modulus (E) affords great insight into the characteristics of the rock. However, its direct determination in the laboratory is costly and time-consuming. Therefore, rock deformation prediction through indirect techniques is greatly suggested. This paper describes hybrid particle swarm optimization (PSO)–artificial neural network (ANN) and imperialism competitive algorithm (ICA)–ANN to solve shortcomings of ANN itself. In fact, the influence of PSO and ICA on ANN results in predicting E was studied in this research. By investigating the related studies, the most important parameters of PSO and ICA were identified and a series of parametric studies for their determination were conducted. All models were built using three inputs (Schmidt hammer rebound number, point load index and p-wave velocity) and one output which is E. To have a fair comparison and to show the capability of the hybrid models, a pre-developed ANN model was also constructed to estimate E. Evaluation of the obtained results demonstrated that a higher ability of E prediction is received developing a hybrid ICA–ANN model. Coefficient of determination (R²) values of (0.952, 0.943 and 0.753) and (0.955, 0.949 and 0.712) were obtained for training and testing of ICA–ANN, PSO–ANN and ANN models, respectively. In addition, VAF values near to 100 (95.182 and 95.143 for train and test) were achieved for a developed ICA–ANN hybrid model. The results indicated that the proposed ICA–ANN model can be implemented better in improving performance capacity of ANN model compared to another implemented hybrid model.

     

Diversified models for portfolio selection based on uncertain semivariance

Since the financial markets are complex, sometimes the future security returns are represented mainly based on experts’ estimations due to lack of historical data. This paper proposes a semivariance method for diversified portfolio selection, in which the security returns are given subjective to experts’ estimations and depicted as uncertain variables. In the paper, three properties of the semivariance of uncertain variables are verified. Based on the concept of semivariance of uncertain variables, two types of mean-semivariance diversified models for uncertain portfolio selection are proposed. Since the models are complex, a hybrid intelligent algorithm which is based on 99-method and genetic algorithm is designed to solve the models. In this hybrid intelligent algorithm, 99-method is applied to compute the expected value and semivariance of uncertain variables, and genetic algorithm is employed to seek the best allocation plan for portfolio selection. At last, several numerical examples are presented to illustrate the modelling idea and the effectiveness of the algorithm.     

A Data-Based Feedback Relearning Algorithm for Uncertain Nonlinear Systems

In this paper, a data-based feedback relearning algorithm is proposed for the robust control problem of uncertain nonlinear systems. Motivated by the classical on-policy and off-policy algorithms of reinforcement learning, the online feedback relearning (FR) algorithm is developed where the collected data includes the influence of disturbance signals. The FR algorithm has better adaptability to environmental changes (such as the control channel disturbances) compared with the off-policy algorithm, and has higher computational efficiency and better convergence performance compared with the on-policy algorithm. Data processing based on experience replay technology is used for great data efficiency and convergence stability. Simulation experiments are presented to illustrate convergence stability, optimality and algorithmic performance of FR algorithm by comparison.      

Application of partial mutual information variable selection to ANN forecasting of water quality in water distribution systems

Recent trends in the management of water supply have increased the need for modelling techniques that can provide reliable, efficient, and accurate representation of the complex, non-linear dynamics of water quality within water distribution systems. Statistical models based on artificial neural networks (ANNs) have been found to be highly suited to this application, and offer distinct advantages over more conventional modelling techniques. However, many practitioners utilise somewhat heuristic or ad hoc methods for input variable selection (IVS) during ANN development.This paper describes the application of a newly proposed non-linear IVS algorithm to the development of ANN models to forecast water quality within two water distribution systems. The intention is to reduce the need for arbitrary judgement and extensive trial-and-error during model development. The algorithm utilises the concept of partial mutual information (PMI) to select inputs based on the analysis of relationship strength between inputs and outputs, and between redundant inputs. In comparison with an existing approach, the ANN models developed using the IVS algorithm are found to provide optimal prediction with significantly greater parsimony. Furthermore, the results obtained from the IVS procedure are useful for developing additional insight into the important relationships that exist between water distribution system variables.     

The influence of ARIMA-GARCH parameters in feed forward neural networks prediction

The objective of this article is to find out the influence of the parameters of the ARIMA-GARCH models in the prediction of artificial neural networks (ANN) of the feed forward type, trained with the Levenberg–Marquardt algorithm, through Monte Carlo simulations. The paper presents a study of the relationship between ANN performance and ARIMA-GARCH model parameters, i.e. the fact that depending on the stationarity and other parameters of the time series, the ANN structure should be selected differently. Neural networks have been widely used to predict time series and their capacity for dealing with non-linearities is a normally outstanding advantage. However, the values of the parameters of the models of generalized autoregressive conditional heteroscedasticity have an influence on ANN prediction performance. The combination of the values of the GARCH parameters with the ARIMA autoregressive terms also implies in ANN performance variation. Combining the parameters of the ARIMA-GARCH models and changing the ANN’s topologies, we used the Theil inequality coefficient to measure the prediction of the feed forward ANN.     

Determining the Natural Frequency of Cantilever Beams Using ANN and Heuristic Search

An artificial neural network (ANN) is used to model the frequency of the first mode, using the beam length, the moment of inertia, and the load applied on the beam as input parameters on a database of 100 samples. Three different heuristic optimization methods are used to train the ANN: genetic algorithm (GA), particle swarm optimization algorithm and imperialist competitive algorithm. The suitability of these algorithms in training ANN is determined based on accuracy and runtime performance. Results show that, in determining the natural frequency of cantilever beams, the ANN model trained using GA outperforms the other models in terms of accuracy.     

铅锌烧结过程透气性状态及热状态优化控制

本文针对具有非线性、强耦合、不确定、多约束特性的复杂铅锌密闭鼓风烧结过程，采用神经网络模型实现了透气性状态和烧穿点位置预测，集成惩罚函数法、基于聚类点的并行变步长网格法和最优保存简单遗传算法(OMSGA)进行状态整体优化．算法具有全局收敛性和工业有效性，解决了复杂铅锌烧结过程实时智能优化控制问题，取得了较好的工业应用效果．     

图因子分解的故障节点快速修复

为了提高分布式存储系统中故障节点的修复效率, 提出一种新的部分重复(fractional repetition, FR)码的构造算法. 该算法利用完全图的因子分解进行构造, 称为CGFBFR (complete graph factorization based FR)码. 该算法首先对完全图进行因子分解, 分解完成以后确定完全图的因子分解个数, 根据需要存储数据块的重复度来选择完全图的因子个数, 将完全图选中的因子所有顶点当做分布式存储系统中需要存储的数据块, 然后对选中因子图的边进行标记, 标记的边当做分布式数据节点进行存储. 最后根据选中的因子的顶点和边生成编码矩阵, 在分布式存储系统中按照编码矩阵中的数据对数据块分别进行存储. 实验仿真结果显示, 本文提出的一种新的部分重复码构造算法, 与分布式存储系统中的里所(reed-solomon, RS)码、简单再生码(simple regenerating codes, SRC)以及最新的循环可变部分重复(variable fractional repetition, VFR)码相比, 在系统修复故障节点时, 能够快速地修复故障节点, 有效降低了故障节点的修复带宽开销、修复局部性、修复复杂度, 而且构造过程简单, 同时可以灵活选择构造参数, 广泛适用于分布式存储系统中.     

基于小波分解和RBF网络的三极管电路故障诊断

在分析现有三极管放大电路故障诊断算法不足的基础上,提出了一种基于模型的小波-RBF网络故障诊断算法。在PSPICE环境下建立三极管常见的故障模型,利用多层小波分解优异的时频特性提取故障特征参数,利用RBF强大的非线性分类能力和快速的收敛特性进行了典型共基极放大电路中三极管的软、硬故障诊断仿真。计算及仿真结果显示,这种故障诊断算法具有诊断速度快、诊断正确率高的特点。     

RBFNN versus FFNN for daily river flow forecasting at Johor River,Malaysia

Streamflow forecasting can have a significant economic impact, as this can help in water resources management and in providing protection from water scarcities and possible flood damage. Artificial neural network (ANN) had been successfully used as a tool to model various nonlinear relations, and the method is appropriate for modeling the complex nature of hydrological systems. They are relatively fast and flexible and are able to extract the relation between the inputs and outputs of a process without knowledge of the underlying physics. In this study, two types of ANN, namely feed-forward back-propagation neural network (FFNN) and radial basis function neural network (RBFNN), have been examined. Those models were developed for daily streamflow forecasting at Johor River, Malaysia, for the period (1999–2008). Comprehensive comparison analyses were carried out to evaluate the performance of the proposed static neural networks. The results demonstrate that RBFNN model is superior to the FFNN forecasting model, and RBFNN can be successfully applied and provides high accuracy and reliability for daily streamflow forecasting.     

Enhancement of artificial neural network learning using centripetal accelerated particle swarm optimization for medical diseases diagnosis

In recent decades, artificial neural networks (ANNs) have been extensively applied in different areas such as engineering, medicine, business, education, manufacturing and so on. Nowadays, ANNs are as a hot research in medicine especially in the fields of medical disease diagnosis. To have a high efficiency in ANN, selection of an appropriate architecture and learning algorithm is very important. ANN learning is a complex task and an efficient learning algorithm has a significant role to enhance ANN performance. In this paper, a new meta-heuristic algorithm, centripetal accelerated particle swarm optimization (CAPSO), is applied to evolve the ANN learning and accuracy. The algorithm is based on an improved scheme of particle swarm algorithm and Newton’s laws of motion. The hybrid learning of CAPSO and multi-layer perceptron (MLP) network, CAPSO-MLP, is used to classify the data of nine standard medical datasets of Hepatitis, Heart Disease, Pima Indian Diabetes, Wisconsin Prognostic Breast Cancer, Parkinson’s disease, Echocardiogram, Liver Disorders, Laryngeal 1 and Acute Inflammations. The performance of CAPSO-MLP is compared with those of PSO, gravitational search algorithm and imperialist competitive algorithm on MLP. The efficiency of methods are evaluated based on mean square error, accuracy, sensitivity, specificity, area under the receiver operating characteristics curve and statistical tests of \(t\) -test and Wilcoxon’s signed ranks test. The results indicate that CAPSO-MLP provides more effective performance than the others for medical disease diagnosis especially in term of unseen data (testing data) and datasets with high missing data values.