Predicting the coagulant dosage is especially crucial to the purification process in water treatment plants, directly affecting the quality of the purified water. Nowadays, several mathematical methods have been adopt...
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Predicting the coagulant dosage is especially crucial to the purification process in water treatment plants, directly affecting the quality of the purified water. Nowadays, several mathematical methods have been adopted for the purification process, but their predictive precision and speed still need to be improved. This study applies a novel neural network called the extreme learning machine ( ELM) to predict the coagulant dosage based on certain signification factors of the raw water. Performances are compared between ELM and back-propagation neural networks in this paper. The results show that both neural network algorithms perform well in this application and ELM can realize online prediction due to its short time consumption.
Environmental factors, as incident light, temperature and night/day length mainly determine the dynamics of growth and development of dioecious yerba-mate. The complex interactions among these factors and growth respo...
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Environmental factors, as incident light, temperature and night/day length mainly determine the dynamics of growth and development of dioecious yerba-mate. The complex interactions among these factors and growth responses highlight the need for growth model, which describes plant modifications under natural and stress conditions, accounting for the growth unit formations in male and female individuals. The rhythmic growth of yerba-mate considers the existence of two annual growth flushes, (spring and autumn) and two annual growth pauses (summer and winter). We developed an individual-based ecological model (InterpolMateS1) that incorporates some aspects of growth and development of yerba-mate referent to two cultivation environments - monoculture and forest understory. The environmental time series, together with plant morphological time series and information about periods of rhythmic growth and respective growth pauses, were used for artificial neural network (ANN) training. The back-propagation algorithm was implemented to refine the weights generated in ANN from the monthly organized input and to adjust using expected output morphological data sets. The probability of meristem ability to continue the growth in the next growth unit and to preserve the leaf in each internode along the axes of 1st-3rd branching order was calculated and implemented into the model. The cubic splines interpolation was more accurate to define the growth parameters curves of yerba-mate. The InterpolMateS1 was daily-step programmed to calculate the growth of yerba-mate for biennial period between two subsequent prunings. The software was tested to simulate the reduction in growth and biomass production when long-term stress conditions were applied. Virtual females were found to be more sensitive to changes of environmental conditions than males, when low water availability and low temperatures occurred during spring and autumn growth flushes. (C) 2013 Elsevier B.V. All rights reserved.
For most of the locations all over Egypt the records of diffuse radiation in whatever scale are non-existent. In case that it exists, the quality of these records is not as good as it should be for most purposes and s...
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For most of the locations all over Egypt the records of diffuse radiation in whatever scale are non-existent. In case that it exists, the quality of these records is not as good as it should be for most purposes and so an estimate of its values is desirable. To achieve such a task, an artificial neural network (ANN) model has been proposed to predict diffuse fraction (K-D) in hourly and daily scale. A comparison between the performances of the ANN model with that of two linear regression models has been reported. An attempt was also done to describe the ANN outputs in terms of first order polynomials relating K-D with clearness index (K-T) and sunshine fraction (S/S-0). If care is taken in considering the corresponding regional climatic differences, these correlations can be generalized and transferred to other sites. The results hint that the ANN model is more suitable to predict diffuse fraction in hourly and daily scales than the regression models in the plain areas of Egypt. (c) 2006 Elsevier Ltd. All rights reserved.
The problem of authenticating extra virgin olive oil varieties is particularly important from the standpoint of quality control. After having shown in our previous works the possibility of discriminating oils from a s...
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The problem of authenticating extra virgin olive oil varieties is particularly important from the standpoint of quality control. After having shown in our previous works the possibility of discriminating oils from a single variety using chemometrics, in this study a combination of two different neural networks architectures was employed for the resolution of simulated binary blends of oils from different cultivars. In particular, a Kohonen self-organizing map was used to select the samples to include in the training, test and validation sets, needed to operate the successive calibration stage, which has been carried out by means of several multilayer feed-forward neural networks. The optimal model resulted in a validation Q(2) in the range 0.91-0.96 (10 data sets), corresponding to an average prediction error of about 5-7.5%, which appeared significantly better than in the case of random or Kennard-Stone selection. (C) 2007 Elsevier B.V. All rights reserved.
Due to the complexity and extensive application of wireless systems, fading channel modeling is of great importance for designing a mobile network, especially for high speed environments. High mobility challenges the ...
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Due to the complexity and extensive application of wireless systems, fading channel modeling is of great importance for designing a mobile network, especially for high speed environments. High mobility challenges the speed of channel estimation and model optimization. In this study, we propose a single-hidden layer feedforward neural network (SLFN) approach to modelling fading channels, including large-scale attenuation and small-scale variation. The arrangements of SLFN in path loss (PL) prediction and fading channel estimation are provided, and the information in both of them is trained with extreme learning machine (ELM) algorithm and a faster back-propagation (BP) algorithm called Levenberg-Marquardt algorithm. Computer simulations show that our proposed SLFN estimators could obtain PL prediction and the instantaneous channel transfer function of sufficient accuracy. Furthermore, compared with BP algorithm, the ability of ELM to provide millisecond-level learning makes it very suitable for fading channel modelling in high speed scenarios.
A novel intelligent method based on wavelet neural network (WNN) was proposed to identify the gear crack degradation in gearbox in this paper. The wavelet packet analysis (WPA) is applied to extract the fault feature ...
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A novel intelligent method based on wavelet neural network (WNN) was proposed to identify the gear crack degradation in gearbox in this paper. The wavelet packet analysis (WPA) is applied to extract the fault feature of the vibration signal, which is collected by two acceleration sensors mounted on the gearbox along the vertical and horizontal direction. The back-propagation (BP) algorithm is studied and applied to optimize the scale and translation parameters of the Morlet wavelet function, the weight coefficients, threshold values in WNN structure. Four different gear crack damage levels under three different loads and three various motor speeds are presented to obtain the different gear fault modes and gear crack degradation in the experimental system. The results show the feasibility and effectiveness of the proposed method by the identification and classification of the four gear modes and degradation.
In this study, an inverse method is proposed for estimating the boundary conditions in a heat conduction problem using a regression analysis, neural network trained by a local optimizer and lastly, that trained by the...
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In this study, an inverse method is proposed for estimating the boundary conditions in a heat conduction problem using a regression analysis, neural network trained by a local optimizer and lastly, that trained by the local and global optimizers simultaneously. The test problem consists of a square slab with an internal heat source of circular shape. Once the boundary conditions of the square slab and periphery of the heat source are specified, the temperature can be estimated for two-dimensional heat conduction problems. This constitutes the forward heat transfer problem. Reverse heat transfer problem is formulated to determine the location of the heat source from some known temperature values elsewhere in the slab. A reasonably good solution is obtained from the said inverse problem using the proposed approach, whose performance is also compared with that of other two approaches.
This paper proposes a TSK-type fuzzy neural network system (TFNN) for identifying and controlling nonlinear control benchmark problem system. It is available for nonlinear dynamic system with uncertainties. The TFNN s...
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This paper proposes a TSK-type fuzzy neural network system (TFNN) for identifying and controlling nonlinear control benchmark problem system. It is available for nonlinear dynamic system with uncertainties. The TFNN system can construct and learn its knowledge base from the input-output training data firstly. Thus, a nonlinear system can be represented by several if-then rules with Gaussian membership functions and TSK-type consequent parts. Based on the learned TFNN system, a robust fuzzy controller is proposed, which combines linear matrix inequality-based fuzzy controller and fuzzy sliding model controller. Rigorous proof of asymptotic stability for the closed-loop system is presented via Lyapunov stability theorem. Several examples are presented to illustrate the effectiveness of our approach.
Engineered fabric manufacturing needs a thorough understanding of the functional properties and their key control construction parameters. When the relationship between a set of interrelated properties goes out of the...
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Engineered fabric manufacturing needs a thorough understanding of the functional properties and their key control construction parameters. When the relationship between a set of interrelated properties goes out of the complete comprehension of human brain, neural networks could be used to find the unknown function. This article describes the method of applying the artificial neural network for the prediction performance parameters for airbag fabrics. The results of the ANN performance prediction had low prediction error i.e., 12% with all the samples and the artificial neural network based on Error back-propagation were found promising for a new domain of design prediction technique. The prediction performance of the neural network was based on the amount of training given to it, i.e., the diversity of the data and the amount of data;resulting in better the mapping of the network, and better predictions. Airbag fabrics could be successfully engineered using artificial neural network.
The prediction of clinical outcome of patients after breast cancer surgery plays an important role in medical tasks such as diagnosis and treatment planning. Different prognostic factors for breast cancer outcome appe...
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The prediction of clinical outcome of patients after breast cancer surgery plays an important role in medical tasks such as diagnosis and treatment planning. Different prognostic factors for breast cancer outcome appear to be significant predictors for overall survival, but probably form part of a bigger picture comprising many factors. Survival estimations are currently performed by clinicians using the statistical techniques of survival analysis. In this sense, artificial neural networks are shown to be a powerful tool for analysing datasets where there are complicated non-linear interactions between the input data and the information to be predicted. This paper presents a decision support tool for the prognosis of breast cancer relapse that combines a novel algorithm TDIDT (control of induction by sample division method, CIDIM), to select the most relevant prognostic factors for the accurate prognosis of breast cancer, with a system composed of different neural networks topologies that takes as input the selected variables in order for it to reach good correct classification probability. In addition, a new method for the estimate of Bayes' optimal error using the neural network paradigm is proposed. Clinical-pathological data were obtained from the Medical Oncology Service of the Hospital Clinico Universitario of Malaga, Spain. The results show that the proposed system is an useful tool to be used by clinicians to search through large datasets seeking subtle patterns in prognostic factors, and that may further assist the selection of appropriate adjuvant treatments for the individual patient. (C) 2002 Elsevier Science B.V. All rights reserved.
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