Evolving artificial neural network and imperialist competitive algorithm for prediction oil flow rate of the reservoir

Multiphase flow meters (MPFMs) are utilized to provide quick and accurate well test data in numerous numbers of oil production applications like those in remote or unmanned locations topside exploitations that minimize platform space and subsea applications. Flow rates of phases (oil, gas and water) are most important parameter which is detected by MPFMs. Conventional MPFM data collecting is done in long periods; because of radioactive sources usage as detector and unmanned location due to wells far distance. In this paper, based on a real case of MPFM, a new method for oil rate prediction of wells base on Fuzzy logic, Artificial Neural Networks (ANN) and Imperialist Competitive Algorithm is presented. Temperatures and pressures of lines have been set as input variable of network and oil flow rate as output. In this case a 1600 data set of 50 wells in one of the northern Persian Gulf oil fields of Iran were used to build a database. ICA-ANN can be used as a reliable alternative way without personal and environmental problems. The performance of the ICA-ANN model has also been compared with ANN model and Fuzzy model. The results prove the effectiveness, robustness and compatibility of the ICA-ANN model.     

A computational intelligence‐based approach for short‐term traffic flow prediction

This paper proposes alternative approaches for the prediction of short‐term traffic flow using three branches of computational intelligence techniques, namely linear genetic programming (LGP), multilayer perceptron (MLP) and fuzzy logic (FL). Different LGP, MLP and FL models are developed for estimating the 5‐ and 30‐min traffic flow rates. New LGP‐ and MLP‐based prediction equations are derived for the traffic flow rates in the 5‐ and 30‐min time intervals. The models are established upon extensive databases of the traffic flow records obtained from Iran's Rasht‐Qazvin highway. The results indicate that the proposed models are effectively capable of predicting the target values. The LGP‐based models are found to be simple, straightforward and more practical for predictive purposes compared with the other derived models.     

基于Sugeno型神经模糊系统的交通流状态预测算法

从交通流状态的模糊特性出发,设计基于Sugeno型神经模糊系统的交通流状态预测算法.选择交通流状态的影响指标作为模糊推理系统的输入、交通流状态作为输出;据经验对输入、输出划分模糊子集,给出相应的隶属度函数并制定模糊规则;建立具有5层结构的神经模糊推理系统,利用神经网络优化调整模糊推理系统的隶属度函数和模糊规则.仿真实验表明,神经网络可直接优化模糊推理系统的隶属度函数,通过对连接权值的训练间接优化模糊规则,故Sugeno型神经模糊系统相比常规模糊系统具有更好的交通流状态预测性能.     

Novel metaheuristic classification approach in developing mathematical model-based solutions predicting failure in shallow footing

This study evaluated and compared several novel classification approaches to develop the most reliable stability model-based solution in the prediction of shallow footing’s allowable settlement. By applying the biogeography-based algorithm, this study presents an optimized metaheuristic classification approach with mathematical-based multi-layer perceptron neural network and fuzzy inference system to achieve a better assessment of the recognition of a complex failure phenomenon. By the contribution of a large number of finite element simulation, and considering seven key factors, the settlement of a shallow footing placed on a two-layered soil was measured as the target variable. Then, to change into the classification method, two overall situations of stability or failure were considered for the proposed soil layer. The ensemble of BBO–MLP and BBO–FIS are developed, and the results are evaluated by well-known accuracy indices. The results showed that employing BBO helps both MLP and FIS to have a better analysis. Besides, referring to the obtained total ranking scores of 6, 5, 11, and 8, respectively, for the MLP, FIS, BBO–MLP, and BBO–FIS, the BBO–MLP found to be the most accurate model, followed by BBO–FIS, MLP, and FIS.

     

River flow forecasting through nonlinear local approximation in a fuzzy model

This study investigates the potential of nonlinear local function approximation in a Takagi–Sugeno (TS) fuzzy model for river flow forecasting. Generally, in a TS framework, the local approximation is performed by a linear model, while in this approach, linear function approximation is substituted using a nonlinear function approximation. The primary hypothesis herein is that the process being modeled (rainfall–runoff in this study) is highly nonlinear, and a linear approximation at the local domain might still leave a lot of unexplained variance by the model. In this study, subtractive clustering technique is used for domain partition, and neural network is used for function approximation. The modeling approach has been tested on two case studies: Kolar basin in India and Kentucky basin in USA. The results of fuzzy nonlinear local approximation (FNLLA) model are highly promising. The performance of the FNLLA is compared with that of a pure fuzzy inference system (FIS), and it is observed that both the models perform similar at 1-step-ahead forecasts. However, the FNLLA performs much better than FIS at higher lead times. It is also observed that FNLLA forecasts the river flow with lesser error compared to FIS. In the case of Kolar River, more than 40 % of the total data are forecasted with <2 % error by FNLLA at 1 h ahead, while the corresponding value for FIS is only 20 %. In the case of 3-h-ahead forecasts, these values are 25 % for FNLLA and 15 % for FIS. Performance of FNLLA in the case of Kentucky River basin was also better compared to FIS. It is also found that FNLLA simulates the peak flow better than FIS, which is certainly an improvement over the existing models.     

Multilayer perceptron, fuzzy sets, and classification

A fuzzy neural network model based on the multilayer perceptron, using the backpropagation algorithm, and capable of fuzzy classification of patterns is described. The input vector consists of membership values to linguistic properties while the output vector is defined in terms of fuzzy class membership values. This allows efficient modeling of fuzzy uncertain patterns with appropriate weights being assigned to the backpropagated errors depending upon the membership values at the corresponding outputs. During training, the learning rate is gradually decreased in discrete steps until the network converges to a minimum error solution. The effectiveness of the algorithm is demonstrated on a speech recognition problem. The results are compared with those of the conventional MLP, the Bayes classifier, and other related models.     

Hierarchical genetic optimization of a fuzzy logic system for energy flows management in microgrids

Bio-inspired algorithms like Genetic Algorithms and Fuzzy Inference Systems (FIS) are nowadays widely adopted as hybrid techniques in improving goods and services. In this paper we present an interesting application of the fuzzy-GA paradigm to the problem of energy flows management in microgrids, concerning the design, through a data driven synthesis procedure, of an Energy Management System (EMS). The main aim consists in performing decision making for power flow management tasks in the proposed microgrid model, equipped by renewable sources and an energy storage system, aiming to maximize the accounting profit in energy trading with the main-grid. In particular this study focuses on the application of a Hierarchical Genetic Algorithm (HGA) for tuning the Rule Base (RB) of a Fuzzy Inference System (FIS), trying to discover a minimal fuzzy rules set as the core inference engine of an an EMS. The HGA rationale focuses on a particular encoding scheme, based on control genes and parametric genes, applied to the optimization of the FIS parameters, allowing to perform a reduction in the structural complexity of the RB. A performance comparison is performed with a simpler approach based on a classic fuzzy-GA scheme, where both FIS parameters and rule weights are tuned, while the number of fuzzy rules is fixed in advance. Experiments shows how the fuzzy-HGA approach adopted for the synthesis of the proposed controller outperforms the classic fuzzy-GA scheme, increasing the accounting profit by 67% in the considered energy trading problem, yielding at the same time a simpler RB.     

Gaussian kernel-based fuzzy inference systems for high dimensional regression

We propose a novel architecture for a higher order fuzzy inference system (FIS) and develop a learning algorithm to build the FIS. The consequent part of the proposed FIS is expressed as a nonlinear combination of the input variables, which can be obtained by introducing an implicit mapping from the input space to a high dimensional feature space. The proposed learning algorithm consists of two phases. In the first phase, the antecedent fuzzy sets are estimated by the kernel-based fuzzy c-means clustering. In the second phase, the consequent parameters are identified by support vector machine whose kernel function is constructed by fuzzy membership functions and the Gaussian kernel. The performance of the proposed model is verified through several numerical examples generally used in fuzzy modeling. Comparative analysis shows that, compared with the zero-order fuzzy model, first-order fuzzy model, and polynomial fuzzy model, the proposed model exhibits higher accuracy, better generalization performance, and satisfactory robustness.     

Application of temporal difference learning rules in short‐term traffic flow prediction

Studying dynamic behaviours of a transportation system requires the use of the system mathematical models as well as prediction of traffic flow in the system. Therefore, traffic flow prediction plays an important role in today's intelligent transportation systems. This article introduces a new approach to short‐term daily traffic flow prediction based on artificial neural networks. Among the family of neural networks, multi‐layer perceptron (MLP), radial basis function (RBF) neural network and wavenets have been selected as the three best candidates for performing traffic flow prediction. Moreover, back‐propagation (BP) has been adapted as the most efficient learning scheme in all the cases. It is shown that the coefficients produced by temporal signals improve the performance of the BP learning (BPL) algorithm. Temporal signals provide researchers with a new model of temporal difference BP learning algorithm (TDBPL). The capability and performance of TDBPL algorithm are examined by means of simulation in order to prove that the wavelet theory, with its multi‐resolution ability in comparison to RBF neural networks, is a suitable algorithm in traffic flow forecasting. It is also concluded that despite MLP applications, RBF neural networks do not provide negative forecasts. In addition, the local minimum problems are inevitable in MLP algorithms, while RBF neural networks and wavenet networks do not encounter them.     

Assessment of plastic zones surrounding the power station cavern using numerical,fuzzy and statistical models

Plastic zones evaluation around the powerhouse caverns is a very crucial issue in designing and constructing these structures and accurate determination of their related optimum support systems. Due to inherent difficulties during the field measurement of plastic zones around the powerhouse caverns and shortcomings of the available methods in this field, applying new predictive models is an attractive and helpful topic. Accordingly, plastic zones around the powerhouse caverns have been investigated in this research using numerical analysis (NA), fuzzy inference system (FIS) and multivariate regression (MVR) model. Based on the numerical simulations, a new predictive equation has been developed to determine the plastic zone at middle point of sidewall and induced key point around a cavern. The basic parameters including rock geomechanical properties and geometrical characteristics of cavern structures have been considered as input variables in plastic zones modeling at middle points of roof, floor, left sidewall and right sidewall as well as at key point. For FIS and MVR models construction, sufficient datasets were introduced based on the numerical simulations. Performance of established models has been assessed applying testing dataset and utilizing powerful statistical indices. Accordingly, it is proved that the derived results from FIS and NA models are more precise than MVR model and they are more satisfactory in plastic zone estimation. Finally, parametric study results revealed that lateral stress coefficient, depth of overburden and rock mass rating are the most effectual parameters and tensile strength is the least influencing parameter on the plastic zone around a cavern.

     

A Cascaded Fuzzy Inference System for Indian river water quality prediction

Now-a-days, Fuzzy Inference System (FIS) is considered as an effective tool for solution of many complex engineering systems when ambiguity and uncertainly is associated with the systems. Mamdani and Takagi, Sugeno and Kang (TSK) models poses simplicity in modeling but their system performance prediction capability is severely affected as complexity of the problem increases. In a multi-input, multi-output situation where a system consists of many subsystems and different outputs are desired from each subsystem, an improved version of FIS must be adopted rather than developing FIS for each subsystem. When dealing with such a system, it is prudent to use cascading systems rather than developing models for individual systems. To this end, a new Cascaded Mamdani Fuzzy Inference System is proposed in this paper and its performance is evaluated with the help of prediction of Indian River water quality index (WQI). In general, WQI value is a dimensionless number ranging from 0 to 100 (best quality) and plays an important role in evaluating the water quality of rivers. The proposed model is designed to predict WQI for five rivers in India. The cascaded fuzzy system simplifies and speeds up the computation of WQI as compared to the currently existing standards. In this paper, the proposed model is compared with three International water quality criteria and it is found that the designed model results in accurate prediction.     

基于非单点模糊正则网络的图像预测编码

提出了一种利用非单点模糊正则网络构建预测器的图像压缩预测编码算法。该算法将非单点模糊化技术引入正则神经网络,在自学习的过程中,能够自动滤除训练数据中的噪声,获取准确的信息。通过仿真试验,并与传统预测编码方法、神经网络预测编码方法进行比较,结果证明该算法具有抗干扰能力强、预测精度高、恢复图像效果好等突出优点。     

GSM churn management by using fuzzy c-means clustering and adaptive neuro fuzzy inference system

Churn management is important and critical issue for Global Services of Mobile Communications (GSM) operators to develop strategies and tactics to prevent its subscribers to pass other GSM operators. First phase of churn management starts with profile creation for the subscribers. Profiling process evaluates call detail data, financial information, calls to customer service, contract details, market details and geographic and population data of a given state. In this study, input features are clustered by x-means and fuzzy c-means clustering algorithms to put the subscribers into different discrete classes. Adaptive Neuro Fuzzy Inference System (ANFIS) is executed to develop a sensitive prediction model for churn management by using these classes. First prediction step starts with parallel Neuro fuzzy classifiers. After then, FIS takes Neuro fuzzy classifiers’ outputs as input to make a decision about churners’ activities.     

A hybrid model based on adaptive-network-based fuzzy inference system to forecast Taiwan stock market

In recent years, many academy researchers have proposed several forecasting models based on technical analysis to predict models such as Engle, 1982, Cheng et al., 2010. After reviewing the literature, two major drawbacks are found in past models: (1) the forecasting models based on artificial intelligence algorithms (AI), such as neural networks (NN) and genetic algorithms (GAs), produce complex and unintelligible rules; and (2) statistic forecasting models, such as time series, require some basic assumptions for variables and build forecasting models based on mathematic equations, which are not easily understandable by stock investors. In order to refine these drawbacks of past models, this paper has proposed a model, based on adaptive-network-based fuzzy inference system which uses multi-technical indicators, to predict stock price trends. Three refined processes have proposed in the hybrid model for forecasting: (1) select essential technical indicators from popular indicators by a correlation matrix; (2) use the subtractive clustering method to partition technical indicator value into linguistic values based on an data discretization method; (3) employ a fuzzy inference system (FIS) to extract rules of linguistic terms from the dataset of the technical indicators, and optimize the FIS parameters based on an adaptive network to produce forecasts. A six-year period of the TAIEX is employed as experimental database to evaluate the proposed model with a performance indicator, root mean squared error (RMSE). The experimental results have shown that the proposed model is superior to two listing models (Chen’s and Yu’s models).     

Fuzzy association rule mining approaches for enhancing prediction performance

This paper presents an investigation into two fuzzy association rule mining models for enhancing prediction performance. The first model (the FCM–Apriori model) integrates Fuzzy C-Means (FCM) and the Apriori approach for road traffic performance prediction. FCM is used to define the membership functions of fuzzy sets and the Apriori approach is employed to identify the Fuzzy Association Rules (FARs). The proposed model extracts knowledge from a database for a Fuzzy Inference System (FIS) that can be used in prediction of a future value. The knowledge extraction process and the performance of the model are demonstrated through two case studies of road traffic data sets with different sizes. The experimental results show the merits and capability of the proposed KD model in FARs based knowledge extraction. The second model (the FCM–MSapriori model) integrates FCM and a Multiple Support Apriori (MSapriori) approach to extract the FARs. These FARs provide the knowledge base to be utilized within the FIS for prediction evaluation. Experimental results have shown that the FCM–MSapriori model predicted the future values effectively and outperformed the FCM–Apriori model and other models reported in the literature.     

Online tuning of fuzzy inference systems using dynamic fuzzy Q-learning

This paper presents a dynamic fuzzy Q-learning (DFQL) method that is capable of tuning fuzzy inference systems (FIS) online. A novel online self-organizing learning algorithm is developed so that structure and parameters identification are accomplished automatically and simultaneously based only on Q-learning. Self-organizing fuzzy inference is introduced to calculate actions and Q-functions so as to enable us to deal with continuous-valued states and actions. Fuzzy rules provide a natural mean of incorporating the bias components for rapid reinforcement learning. Experimental results and comparative studies with the fuzzy Q-learning (FQL) and continuous-action Q-learning in the wall-following task of mobile robots demonstrate that the proposed DFQL method is superior.     

Robust fuzzy control for continuous perturbed time‐delay affine takagi–sugeno fuzzy models

The stability analysis and controller synthesis methodology for a continuous perturbed time‐delay affine (CPTDA) Takagi–Sugeno (T‐S) fuzzy model is proposed in this paper. The CPTDA T‐S fuzzy models include both linear nominal parts and uncertain parameters in each fuzzy rule. The proposed fuzzy control approach is developed based on an iterative linear matrix inequality (ILMI) algorithm to cope with the stability criteria and H_∞ performance constraints for the CPTDA T‐S fuzzy models. Finally, a numerical simulation for the nonlinear inverted pendulum system is given to show the application and availability of the present design approach. Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Modified Gath-Geva fuzzy clustering for identification of Takagi-Sugeno fuzzy models

The construction of interpretable Takagi-Sugeno (TS) fuzzy models by means of clustering is addressed. First, it is shown how the antecedent fuzzy sets and the corresponding consequent parameters of the TS model can be derived from clusters obtained by the Gath-Geva (GG) algorithm. To preserve the partitioning of the antecedent space, linearly transformed input variables can be used in the model. This may, however, complicate the interpretation of the rules. To form an easily interpretable model that does not use the transformed input variables, a new clustering algorithm is proposed, based on the expectation-maximization (EM) identification of Gaussian mixture models. This new technique is applied to two well-known benchmark problems: the MPG (miles per gallon) prediction and a simulated second-order nonlinear process. The obtained results are compared with results from the literature.     

基于改进在线极限学习机的短时交通流预测模型研究

交通流信息预测是智能交通系统进行交通疏导管理的重要基础,为城市交通管理规划提供可靠的数据支持和科学的决策依据。由于交通流量数据是实时更新的增量流数据,每次更新历史数据集时都需要重新构建预测模型,消耗了大量计算资源和运行时间,为此提出一种基于改进在线顺序极限学习机的交通流预测模型（IOS-ELM）,通过构建新增数据的增强特征映射关系,生成交通流动态更新特征表示空间,实现短时交通流预测模型的动态更新。利用长沙市远大一路交通流数据评估该模型,实验结果表明,IOS-ELM模型在NRMSE和MAPE的预测性能上均超过其他基准预测模型（MLP、ELM、OS-ELM和SVR）,同时模型的预测耗时较小,可以保证一定实时性,满足城市道路交通流的实时准确预测的需求。     

Predictive functional control based on fuzzy model forheat-exchanger pilot plant

In this paper, a new method of predictive control is presented. In this approach, a well-known method of predictive functional control is combined with fuzzy model of the process. The prediction is based on fuzzy model given in the form of Takagi-Sugeno type. The proposed fuzzy predictive control has been evaluated by implementation on heat-exchanger plant, which exhibits a strong nonlinear behavior. It has been shown that in the case of nonlinear processes, the approach using fuzzy predictive control gives very promising results. The proposed approach is potentially interesting in the case of batch reactors, heat-exchangers, furnaces, and all the processes that are difficult to model