This paper intended to offer an architecture of artificial neural networks (NNs) for finding approximate solution of a second kind linear Fredholm integral equations system. For this purpose, first we substitute the N...
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This paper intended to offer an architecture of artificial neural networks (NNs) for finding approximate solution of a second kind linear Fredholm integral equations system. For this purpose, first we substitute the N-th truncation of the Taylor expansion for unknown functions in the origin system. By applying the suggested neural network for adjusting the real coefficients of given expansions in resulting system. The proposed NN is a two-layer feedback neural network such that it can get a initial vector and then calculates it's corresponding output vector. In continuance, a cost function is defined by using output vector and the target outputs. Consequently, the reported NN using a learning algorithm that based on the gradient descent method, will adjust the coefficients in given Taylor series. Eventually, we have showed this method in comparison with existing numerical methods such as trapezoidal quadrature rule provides solutions with good generalization and high accuracy. The proposed method is illustrated by several examples with computer simulations. (C) 2012 Elsevier Inc. All rights reserved.
Power management in homes and offices requires appliance usage prediction when the future user requests are not available. The randomness and uncertainties associated with an appliance usage make the prediction of app...
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Power management in homes and offices requires appliance usage prediction when the future user requests are not available. The randomness and uncertainties associated with an appliance usage make the prediction of appliance usage from energy consumption data a non-trivial task. A general model for prediction at the appliance level is still lacking. This work proposes to improve learning algorithms with expert knowledge and proposes a general model using a knowledge driven approach to forecast if a particular appliance will start during a given hour or not. The approach is both a knowledge driven and data driven one. The overall energy management for a house requires that the prediction is done for the next 24 h in the future. The proposed model is tested over the IRISE data and using different machine learning algorithms. The results for predicting the next hour consumption are presented, but the model works also for predicting the next 24 h. (C) 2013 Elsevier B.V. All rights reserved.
In this study, the artificial neural network (ANN) was used for the prediction of WDT. The inputs to network are molar mass and pressure, and the output is WDT at each input. A two-layer network with different hidden ...
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In this study, the artificial neural network (ANN) was used for the prediction of WDT. The inputs to network are molar mass and pressure, and the output is WDT at each input. A two-layer network with different hidden neurons and different learning algorithms such as LM, SCG, GDA and BR were examined. The network with 16 hidden neurons and Levenberg-Marquardt (LM) train function showed the best results in comparison with the other networks. Also, the predicted results of this network were compared with the thermodynamic models and better accordance with experimental data for ANN was concluded. (C) 2013 Elsevier B.V. All rights reserved.
We present a unified convergence analysis, based on a deterministic discrete time (DDT) approach, of the normalized projection approximation subspace tracking (Normalized PAST) algorithms for estimating principal and ...
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We present a unified convergence analysis, based on a deterministic discrete time (DDT) approach, of the normalized projection approximation subspace tracking (Normalized PAST) algorithms for estimating principal and minor components of an input signal. The proposed analysis shows that the DDT system of the Normalized PAST algorithm (for PCA/MCA), with any forgetting factor in a certain range, converges to a desired eigenvector. This eigenvector is completely characterized as the normalized version of the orthogonal projection of the initial estimate onto the eigensubspace corresponding to the largest/smallest eigenvalue of the autocorrelation matrix of the input signal. This characterization holds in general case where the eigenvalues are not necessarily distinct. Numerical examples show that the proposed analysis demonstrates very well the convergence behavior of the Normalized PAST algorithms which uses a rank-1 instantaneous approximation of the autocorrelation matrix. (C) 2012 Elsevier B.V. All rights reserved.
Fuzzy neural network (FNN) can be trained with crisp and fuzzy data. This paper presents a novel approach to solve system of fuzzy differential equations (SFDEs) with fuzzy initial values by applying the universal app...
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Fuzzy neural network (FNN) can be trained with crisp and fuzzy data. This paper presents a novel approach to solve system of fuzzy differential equations (SFDEs) with fuzzy initial values by applying the universal approximation method (UAM) through an artificial intelligence utility in a simple way. The model finds the approximated solution of SFDEs inside of its domain for the close enough neighborhood of the fuzzy initial points. We propose a learning algorithm from the cost function for adjusting of fuzzy weights. At the same time, some examples in engineering and economics are designed. (C) 2013 Elsevier B. V. All rights reserved.
Background: Public databases such as the NCBI Gene Expression Omnibus contain extensive and exponentially increasing amounts of high-throughput data that can be applied to molecular phenotype characterization. Collect...
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Background: Public databases such as the NCBI Gene Expression Omnibus contain extensive and exponentially increasing amounts of high-throughput data that can be applied to molecular phenotype characterization. Collectively, these data can be analyzed for such purposes as disease diagnosis or phenotype classification. One family of algorithms that has proven useful for disease classification is based on relative expression analysis and includes the Top-Scoring Pair (TSP), k-Top-Scoring Pairs (k-TSP), Top-Scoring Triplet (TST) and Differential Rank Conservation (DIRAC) algorithms. These relative expression analysis algorithms hold significant advantages for identifying interpretable molecular signatures for disease classification, and have been implemented previously on a variety of computational platforms with varying degrees of usability. To increase the user-base and maximize the utility of these methods, we developed the program AUREA (Adaptive Unified Relative Expression Analyzer)-a cross-platform tool that has a consistent application programming interface (API), an easy-to-use graphical user interface (GUI), fast running times and automated parameter discovery. Results: Herein, we describe AUREA, an efficient, cohesive, and user-friendly open-source software system that comprises a suite of methods for relative expression analysis. AUREA incorporates existing methods, while extending their capabilities and bringing uniformity to their interfaces. We demonstrate that combining these algorithms and adaptively tuning parameters on the training sets makes these algorithms more consistent in their performance and demonstrate the effectiveness of our adaptive parameter tuner by comparing accuracy across diverse datasets. Conclusions: We have integrated several relative expression analysis algorithms and provided a unified interface for their implementation while making data acquisition, parameter fixing, data merging, and results analysis 'point- and -click' simple.
In this study, two different surface shaped solar air collectors are constructed and examined experimentally;corrugated and trapeze shaped. Experiments are carried out between 09.00 and 17.00 in October under the prev...
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In this study, two different surface shaped solar air collectors are constructed and examined experimentally;corrugated and trapeze shaped. Experiments are carried out between 09.00 and 17.00 in October under the prevailing weather conditions of Elazig, Turkey. Thermal performances belonging to experimental systems are calculated by using data obtained from experiments. A feed-forward neural network based on back propagation algorithm was developed to predict thermal performances of solar air collectors. The measured data and calculated performance values are used at the design of Levenberg-Marquardt (LM). Calculated values of thermal performances are compared to predicted values. It is concluded that, ANN can be used for prediction of thermal performances of solar air collectors as an accurate method in this system. (C) 2012 Elsevier Ltd. All rights reserved.
We propose in this paper an extended model of the random neural networks, whose architecture is multi-feedback. In this case, we suppose different layers where the neurons have communication with the neurons of the ne...
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We propose in this paper an extended model of the random neural networks, whose architecture is multi-feedback. In this case, we suppose different layers where the neurons have communication with the neurons of the neighbor layers. We present its learning algorithm and its possible utilizations;specifically, we test its use in an encryption mechanism where each layer is responsible of a part of the encryption or decryption process. The multilayer random neural network is a stochastic neural model, in this way the entire proposed encryption model has that feature.
In the paper a five-layers architecture of hybrid wavelet-neuro-fuzzy system which is using the adaptive W-neurons as the nodes is proposed. W-neuron is a neuron which structure is similar to a radial basis functions ...
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In the paper a five-layers architecture of hybrid wavelet-neuro-fuzzy system which is using the adaptive W-neurons as the nodes is proposed. W-neuron is a neuron which structure is similar to a radial basis functions network, but instead of conventional radial basis functions we used multidimensional adaptive wavelet activation-membership functions. The distinctive feature of the proposed system is usage of the wavelets as membership functions in the antecedent layer, and the adaptive multidimensional wavelets as activation functions in the consequent layer that can tune not only the dilation and translation parameters but also its own form during the learning process. The learning algorithms for all antecedent and consequent functions' parameters that have both following and filtering properties are proposed. The experimental results have shown that this wavelet-neuro-fuzzy system has improved approximation properties and has a higher learning rate in comparison with usual wavelet-neuro-fuzzy networks. The proposed hybrid wavelet-neuro-fuzzy system can be used to solve tasks of diagnosis, forecasting, emulation, and identification of nonlinear chaotic and stochastic non-stationary processes. (C) 2012 Elsevier Inc. All rights reserved.
Aiming at the problem that it is difficult for BP algorithm to converge because of more parameters in training of process neural networks based on orthogonal basis expansion, a quantum shuffled frog leaping algorithm ...
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ISBN:
(纸本)9781479905614
Aiming at the problem that it is difficult for BP algorithm to converge because of more parameters in training of process neural networks based on orthogonal basis expansion, a quantum shuffled frog leaping algorithm is presented which combines the quantum theory and is to train the process neural network. In this algorithm, the individuals are expressed with Bloch spherical coordinates of qubits. The quantum individuals are updated by quantum rotation gates, and the mutation of individuals is achieved with Hadamard gates. For the size and direction of rotation angle of quantum rotation gates, a simple determining method is proposed. Above operations extend the search of the solution space effectively. To predict sunspot as an example to validate the presented algorithm.
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