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This paper describes very short-term wind prediction for power generation, utilizing a case study from Tasmania, Australia. Windpower presently is the fastest growing power generation sector in the world. However, windpower is intermittent. To be able to trade efficiently, make the best use of transmission line capability, and address concerns with system frequency in a re-regulated system, accurate very short-term forecasts are essential. The research introduces a novel approach—the application of an adaptive neuro-fuzzy inference system to forecasting a wind time series. Over the very short-term forecast interval, both windspeed and wind direction are important parameters. To be able to be gain the most from a forecast on this time scale, the turbines must be directed toward on oncoming wind. For this reason, this paper forecasts wind vectors, rather than windspeed or power output. [ABSTRACT FROM AUTHOR]

The authors present an approach based on a one-step method using soft computing techniques for a quality assurance process in the form of dimensional checking parameters via industrial image processing. This method offers a high grade of precision in processes to solve the evaluation problems on-line applications. As well known to the researchers, the approaches in a single iteration of these techniques for artificial neural networks (ANN) such as adaptive neuro fuzzy inference systems (ANFIS) and radial basis function network (RBFN) are not documented in the literature. This work provides the simplification to one-step that provides the chance of creation and implementation of these models for online applications without loss of time in the iterations needed to adjust the model (training) to generate a fast response. The main objective of this paper is to provide a model capable of approximating the solution of a function that represents the system, that function is based on historical data of the process but the operators that compound the function are unknown. The relations between the inputs and outputs are known, but the interactions between variables are unknown. Based on the literature, the soft computing techniques are trained by trial and error because they do not have a stop criterion; also, the functions that provide an approximation are unknown in most cases. To solve the problem mentioned above, this paper proposes the one-step method without training. It is necessary to approximate the solution avoiding the overshoot and damping produced by classic approaches. The deviation generated is in the order of one standard deviation whose magnitude is in the order of common approaches for image processing as it is documented in literature for the best case RBFN. [ABSTRACT FROM AUTHOR]

Fetal electrocardiogram (ECG) gives information about the health status of fetus and so, an early diagnosis of any cardiac defect before delivery increases the effectiveness of appropriate treatment. In this paper, authors investigate the use of adaptive neuro-fuzzy inference system (ANFIS) with extended Kalman filter for fetal ECG extraction from one ECG signal recorded at the abdominal areas of the mother's skin. The abdominal ECG is considered to be composite as it contains both mother's and fetus' ECG signals. We use extended Kalman filter framework to estimate the maternal component from abdominal ECG. The maternal component in the abdominal ECG signal is a nonlinear transformed version of maternal ECG. ANFIS network has been used to identify this nonlinear relationship, and to align the estimated maternal ECG signal with the maternal component in the abdominal ECG signal. Thus, we extract the fetal ECG component by subtracting the aligned version of the estimated maternal ECG from the abdominal signal. Our results demonstrate the effectiveness of the proposed technique in extracting the fetal ECG component from abdominal signal at different noise levels. The proposed technique is also validated on the extraction of fetal ECG from both actual abdominal recordings and synthetic abdominal recording. [ABSTRACT FROM AUTHOR]

Brain tumors are amongst the top death-leading health conditions worldwide. Biopsy is the most accurate procedure that determines the brain tumor type whether it is malignant or benign. However, biopsy may not be applicable for some patients with brain cancer (BCa) and could be life-threatening. In this paper, an intelligent diagnosticimage-based systems are implemented to assist physicians in making diagnostic decisions about the BCa type without biopsy procedures. A combined method of artificial intelligent systems and MRI image segmentation is proposed as a tumor classification tool. This study employs image filtration and segmentation on a region of interest (ROI) of an MRI image. Then, extract accurate statistical features are fed into four artificial intelligent (AI) systems: Adaptive neuro-fuzzy inference system(ANFIS), Elamn Neural Network (Elman NN), Nonlinear AutoRegressive with exogenous neural networks (NARXNN), and feedforward NN. The four AI classifiers are investigated and tested on 107 patients with brain tumors. The data base of the brain tumor images used in this study contains both malignant and benign cancers. The performance of the four intelligent tumor classifiers is evaluated. It is found that the NARX NN shows best performance with a classification accuracy of 99.1%. The achieved accuracy level is superior and could be very helpful in clinical purposes. [ABSTRACT FROM AUTHOR]

This paper presents the design of a neuro-fuzzy visual servoing controller for robot guiding fabrics with curved edges towards sewing. Fabrics comprising real cloths consist of curved edges of arbitrary curvatures that can not be standardized. To overcome this difficulty, the idea is to train the robot sewing system and to apply this methodology in a real-time operation environment. The proposed approach for robot sewing is based on visual servoing and a learning technique that combines neural networks and fuzzy logic. A novel genetic-oriented clustering method is used to construct the initial FIS models and then, adaptive neuro-fuzzy inference systems allow tuning them so that it is possible to obtain better estimates. Extensive experiments were carried out in order to build data sets using fabrics with curved edges of various curvatures. The proposed model is validated using fabrics that had not been included in the training process and the results demonstrate that the proposed approach is efficient and effective for robot guiding fabrics with arbitrary curved edges towards sewing. [ABSTRACT FROM AUTHOR]

This paper aims to: (1) develop models based on adaptive neuro-fuzzy inference system (ANFIS) able to predict five-day biochemical oxygen demand (BOD5) in Ouizert reservoir; (2) demonstrate the capability of the ANFIS in the practical issues of water quality management; (3) choose the optimal combination of input variables to improve the model performance; (4) compare two ANFIS partition methods, namely subtractive clustering called ANFIS-SC and grid partitioning, called ANFIS-GP. The models were developed using experimental data which were gathered during a ten-year period, at a mean monthly time step (scale). The input data used are total inorganic nitrogen, chemical oxygen demand (COD), total dissolved solid, dissolved oxygen and phosphate; the output is five-day biochemical oxygen demand (BOD5). Results reveal that ANFIS-SC models gave a higher correlation coefficient, a lower root mean square errors (RMSE) and mean absolute errors than the corresponding ANFIS-GP models. We can conclude that ANFIS-SC has supremacy over ANFIS-GP in terms of performance criteria and prediction accuracy for BOD5 estimation. The results showed that COD is the more effective variable for BOD5 estimating than other parameters, hence COD is the major driving factor for BOD5 modelling through ANFIS. [ABSTRACT FROM AUTHOR]

The current paper deals with the performance evaluation of the application of three soft computing algorithms such as adaptive neuro-fuzzy inference system (ANFIS), backpropagation neural network (BPNN), and deep neural network (DNN) in predicting oxygen aeration efficiency (OAE20) of the gabion spillways. Besides, classical equations, namely multivariate linear and nonlinear regressions (MVLR and MVNLR), including previous studies, were also employed in predicting OAE20 of the gabion spillways. The analysis of results showed that the DNN demonstrated relatively lower error values (root mean square error, RMSE = 0.03465; mean square error, MSE = 0.00121; mean absolute error, MAE = 0.02721) and the highest value of correlation coefficient, CC = 0.9757, performed the best in predicting OAE20 of the gabion spillways; however, other applied models, such as ANFIS, BPNN, MVLR, and MVNLR, were giving comparable results evaluated to statistical appraisal metrics of the relative significance of input parameters based on sensitivity investigation, the porosity (n) of gabion materials was observed to be the most critical parameter, and gabion height (P) had the least impact over OAE20 of the spillways. HIGHLIGHTS An experimental study of the aeration performance of gabion spillways was studied.; Soft computing techniques have been used to evaluate the aeration performance of the gabion spillways using an experimental dataset.; DNN was found to be outperforming the model; however, the proposed ANFIS and BPNN models were performing well.; The sensitivity study suggested that the input parameter, i.e. porosity, was the most sensitive parameter.;

The purpose of this paper is to investigate the relationship between adverse events and infrastructure development investments in an active war theater by using soft computing techniques including fuzzy inference systems (FIS), artificial neural networks (ANNs), and adaptive neuro-fuzzy inference systems (ANFIS) where the accuracy of the predictions is directly beneficial from an economic and humanistic point of view. Fourteen developmental and economic improvement projects were selected as independent variables. A total of four outputs reflecting the adverse events in terms of the number of people killed, wounded or hijacked, and the total number of adverse events has been estimated. The results obtained from analysis and testing demonstrate that ANN, FIS, and ANFIS are useful modeling techniques for predicting the number of adverse events based on historical development or economic project data. When the model accuracy was calculated based on the mean absolute percentage error (MAPE) for each of the models, ANN had better predictive accuracy than FIS and ANFIS models, as demonstrated by experimental results. For the purpose of allocating resources and developing regions, the results can be summarized by examining the relationship between adverse events and infrastructure development in an active war theater, with emphasis on predicting the occurrence of events. We conclude that the importance of infrastructure development projects varied based on the specific regions and time period. [ABSTRACT FROM AUTHOR]