Load forecast performs a vital function in scheduling and operation in power system which makes grid smarter. The superiority of forecast of the short-term load has a major influence on the financial functioning of th...
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ISBN:
(数字)9781538624401
ISBN:
(纸本)9781538624418
Load forecast performs a vital function in scheduling and operation in power system which makes grid smarter. The superiority of forecast of the short-term load has a major influence on the financial functioning of the power system as numerous assessments related to these predictions have important financial penalties. The generalized feedforward network is proposed in this paper with backpropagation algorithm for short-term load forecasting. Inputs to the Artificial Neural Network (ANN) model week code, hour of the day and temperature and output of the ANN model is predicted load. Load during weekdays and weekends are different. So, far better forecasting, week codes are used. The performance of the model is evaluated by different types of performance indices.
In this paper,the authors have attempted the identification of real world non-linear time-varying Pressurized Heavy Water Reactor(PHWR) *** nonlinear dynamics is identified using adaptive feedforward neural network **...
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In this paper,the authors have attempted the identification of real world non-linear time-varying Pressurized Heavy Water Reactor(PHWR) *** nonlinear dynamics is identified using adaptive feedforward neural network *** neural network(ANN) has been introduced to solve PHWR nuclear power plant operation complexity in *** Multi-input single-output(MISO) neural systems are designed for the prediction of control rod reactivity,moderator reactivity,moderator level and actual reactor power based on control rod and moderator level dynamics.A severe transient has been imposed in training and prediction phases with proposed *** proposed ANN is developed in *** essence,the proposed adaptive feedforward neural network mimics and identifies the actual nonlinear time-varying dynamics of PHWR system for an operating PHWR-type nuclear power plant in *** proposed ANN is found highly efficient and *** performance of the proposed ANN is investigated and the predicted results are in good agreement with the measured results.
The relationship between the physical properties of metal is often very complex in nature with its chemistry and several other rolling parameters in operation. Non-linear regression models play a very important role i...
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The relationship between the physical properties of metal is often very complex in nature with its chemistry and several other rolling parameters in operation. Non-linear regression models play a very important role in modelling the underlying mechanism, provided it is known. Artificial neural networks provide a wide class of general-purpose and flexible non-linear regression models. The most commonly used neural networks, called multi-layered perceptrons, can vary the complexity of the model from a simple parametric model to a highly flexible nonparametric model. In this particular work, an industry-based data set is used for learning and optimizing the neural network architecture using some well-known algorithms for prediction under neural-net systems. The outcome of the analysis is compared with the results achieved through empirical statistical modelling from its prediction error level and the knowledge of materials science.
This paper investigates the impact of fractional derivatives on the activation functions of an artificial neural network (ANN). Based on the results and analysis, a three-layer backpropagation neural network model wit...
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The aim of this paper was to explore the usefulness of a backpropagation neural network (BNN) to estimate the biodegradability of benzene derivatives. 127 chemicals selected from the BIODEG data bank (Syracuse Researc...
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Wireless sensor networks (WSNs) have many applications in the field of disaster management, military, healthcare and environmental monitoring. Capability of WSNs is further enhanced by the efficient localization algor...
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ISBN:
(纸本)9781467331487
Wireless sensor networks (WSNs) have many applications in the field of disaster management, military, healthcare and environmental monitoring. Capability of WSNs is further enhanced by the efficient localization algorithms. Localization is the process by which a sensor node determines its own location after deployment. Neural approaches are gaining popularity in evolving new localization algorithms that are capable of optimizing various parameters of WSNs. In this paper, we analyse two backpropagation algorithms based on multi-layer Perceptron (MLP) neural network. The network is trained using static sensor nodes placed in a grid with their coordinates known. The input values are distances from each anchor nodes to a particular sensor node. The output is the actual coordinates of the sensor nodes. After training, the network will be able to predict the coordinates of unknown sensor nodes. This MLP model is analyzed for Bayesian regularization and Levenberg-Marquardt training algorithm. Both algorithms are tested for the robustness and cross-validation. The simulation results demonstrate the effectiveness of the proposed model on localization error.
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