artificial neural network approaches
Artificial Neural Network Approaches For Slope Stability
... used neural networks to model the complex relationship between seismic and soil parameters in order to investigate liquefaction ...The neural network used in this work was trained using case records ...
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Water level forecasting through fuzzy logic and artificial neural network approaches
... As is well known, the rainfall-runoff transformation pro- cesses leading to the formation of a flood wave and to the consequent water level evolution in the river, is extremely complex being non-linear, time varying and ...
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Prediction of roadheaders' performance using artificial neural network approaches (MLP and KOSFM)
... artificial neural network (ANN) ...used neural networks for supervised and unsupervised ones, ...MLP network was trained by 3 input parameters including rock mass properties, rock quality ...
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Artificial Neural Network Approaches to the Prediction of Eutrophication and Algal Blooms in Aras Dam, Iran
... contain artificial neurons which receive a number of inputs (either from original data or from the output of other neurons in the ...the neural architecture of the human ...
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Approaches in RSA Cryptosystem Using Artificial Neural Network
... 1977. Neural networks are often used as a powerful discriminating estimator for tasks in function ...a neural-network-based method relies on Radial Basis Function and Levenberg-Marquardt ...
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Artificial Neural Network
... fault. Neural networks and conventional algorithmic computers are not in competition but complement each ...two approaches (normally a conventional computer is used to supervise the neural ...
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FPGA Implementation of Artificial Neural Network
... computing, Artificial Neural Network plays dominant role in the field of Artificial ...of Artificial Neural Network (ANN), several approaches are there and a number ...
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Forecasting with artificial neural network models
... It is also of interest to see how the statistical methodology SA performs in the case of financial series. In this forecasting exercise, I included 3 monthly series of exchange rates: Canadian $ / US $ (series 9), German ...
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An Artificial Neural Network for Wavelet Steganalysis
... steganalysis is most often performed on data that is attached to email, on websites, on flashdrives, on MP3 players, etc. The research presented in this paper is on blind steganalysis of image data using an ...
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Artificial neural network and kalman filter approaches based on arima for daily wind speed forecasting
... In this study, a new hybrid KF-ANN model is proposed based on an ARIMA model to further improve the forecasting accuracy of wind speed. ANN and KF are useful for handling nonlinearity and stochastic uncertainty problems ...
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Building and Improving Artificial Neural Network Classifier
... Fig.9. Iris Flower classification’s accuracy and loss details IV. CONCLUSION AND FUTURE WORK Deep learning approaches are increasing effectively every day. Deep learning contribute fast and efficient solutions in ...
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Parallel computing for artificial neural network training
... Figure 1. (a) shows an example of an ANN with multilayer perceptron, whereas (b) hints its training method. Each layer is (usually) fully interconnected to its adjacent layers. Multilayer perceptrons perform a functional ...
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Robust artificial neural network for reliability analysis
... based approaches. These simulation approaches include: Monte Carlo (MC) [4], and advanced MC such as: Importance Sampling [5], Directional Sampling [6], Line Sampling [7], Subset Simulation [8] ...an ...
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Regime Switching and Artificial Neural Network Forecasting
... 2.3. Out-of-sample forecasting criteria Having estimated the two empirical models discussed above and obtained 1- step ahead out-of-sample forecasts for the out-of-sample period, we proceed to evaluating their relative ...
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Artificial Neural Network Analysis in Food Science
... increasing network size, a result with potential implications for the design and scalability of communication ...these approaches can yield new insights both into the sensory perception of food and the ...
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Artificial Neural Network
... Perceptron learning rule: The perceptron is an algorithm for supervised classification of an input into one of several possible non-binary outputs. The learning algorithm for perceptrons is an online algorithm, in that ...
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Artificial intelligence. Creation of an artificial neural network
... Future floated railway that is something special that will be created. We can say with confidence that railway transport is one of the most important industries, material production, due to its economic advantage, ...
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II. ARTIFICIAL NEURAL NETWORK
... II. ARTIFICIAL NEURAL NETWORK Last two decades has seen advent of Artificial Neural Network which has been successfully applied to various fields of engineering, medical ...
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A Study on Artificial Neural Network
... simple neural network with electrical circuits and got the results very accurate and derived a remarkable ability of neurons to perceive information from complicated and imprecise ...trained neural ...
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Study of Artificial Neural Network
... An Artificial Neural Network (ANN) is a mathematical model that tries to simulate the structure and functionalities of biological neural ...every artificial neural network ...
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