Multilayer perceptron approach is a method that can be used to make predictions. The multilayer perceptron includes weighting coefficients which can be determined by different optimization techniques. The weighting co...
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Multilayer perceptron approach is a method that can be used to make predictions. The multilayer perceptron includes weighting coefficients which can be determined by different optimization techniques. The weighting coefficients between input layer and hidden layer, also between hidden layer and output layer is the important step for the solution of a multilayer perceptron, and optimized weighting coefficient is used for model predictions. Identifying the weighting coefficients of multilayer perceptron with genetic algorithms is called as geno-multilayer perceptron. In this study, geno-multilayer perceptron approach was used to predict significant wave height. For this purpose, geno-multilayer perceptron approach, a relatively new method, was applied to four stations located in the Lake Okeechobee, Florida, in this study. A comparison between the results of two different training (optimization) algorithms namely genetic algorithms and backpropagationalgorithms was performed. The prediction results show that optimized (trained) weighting coefficients by genetic algorithms reveal a relatively better agreement with observed data compared to backpropagationalgorithms. In order to make comparison between observed data and predicted results, statistical indexes including the mean relative error percentages, the mean square errors, the coefficient of efficiency and the chi-square (chi(2)) parameters were used. (C) 2012 Elsevier Ltd. All rights reserved.
Blasting has been one of the most important contributors of mining since the start of mineral extraction and excavation. Along with fragmentation of the rocks, blasting also produces an excess of energy in the form of...
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Blasting has been one of the most important contributors of mining since the start of mineral extraction and excavation. Along with fragmentation of the rocks, blasting also produces an excess of energy in the form of heat and vibration. Due to the spread of the vibration, the surrounding environment gets affected. Therefore, this paper aims to minimize the vibration to reduce the impact of ground vibration happening due to the mine blasting. In order to optimize the blasting parameters, a good predictor of such vibration is to be created. Hence, the paper compares a lot of predictors including empirical formulas and ANNs (Artificial Neural Networks). The best performing predictor has been used as the objective function for the optimization of parameters. Among the various optimization methods, the firefly algorithm proved to be a very good optimizer. Therefore, it was used to optimize the field parameters and implemented. The resulting optimized parameters showed a significant reduction in the ground vibration of 14.58%.
Overshoot, settling and rise time define the timing parameters of a control system. The main challenge is to attempt to reduce these parameters to achieve good control performances. The target is to obtain the optimal...
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ISBN:
(纸本)9780769546193
Overshoot, settling and rise time define the timing parameters of a control system. The main challenge is to attempt to reduce these parameters to achieve good control performances. The target is to obtain the optimal timing values. In this paper, three different approaches are presented to improve the control performances of second order control systems. The first approach is related to the design of a PID controller based on Ziegler-Nichols tuning formula. An optimal fuzzy controller optimized through Genetic algorithms represents the second approach. Following this way, the best membership functions are chosen with the help of the darwinian theory of natural selection. The third approach uses the neural networks to achieve adaptive neuro-fuzzy controllers. In this way, the fuzzy controller assumes self-tuning capability. The results show that the designed PID controller has a very slow rise time. The genetic-fuzzy controller gives good values of overshoot and settling time. The best global results are achieved by neuro-fuzzy controller which presents good values of overshoot, settling and rise time. Moreover, our neuro-fuzzy controller improves the results of some conventional PID and fuzzy controllers.
Artificial neural networks are used to classify the writing system of an unseen glyph. The complexity of the problem necessitates a large network, which hampers the training of the weights. Three hybrid algorithms - c...
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ISBN:
(纸本)9781424478354
Artificial neural networks are used to classify the writing system of an unseen glyph. The complexity of the problem necessitates a large network, which hampers the training of the weights. Three hybrid algorithms - combining evolution and back-propagation learning - are compared to the standard back-propagation algorithm. The results indicate that pure back-propagation is preferable to any of the hybrid algorithms. back-propagation had both the best classification results and the fastest runtime, in addition to the least complex implementation.
Network traffic is a complex and nonlinear process, which is significantly affected by immeasurable parameters and variables. This paper addresses the use of the five-layer fuzzy neural network (FNN) for predicting th...
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ISBN:
(纸本)0780381858
Network traffic is a complex and nonlinear process, which is significantly affected by immeasurable parameters and variables. This paper addresses the use of the five-layer fuzzy neural network (FNN) for predicting the nonlinear network traffic. The structure of this system is introduced in detail. Through training the FNN using back-propagation algorithm with inertial terms the traffic series can be well predicted by this FNN system. We analyze the performance of the FNN in terms of prediction ability as compared with solely neural network. The simulation demonstrates that the proposed INN is superior to the solely neural network systems. In addition, FNN with different fuzzy reasoning approaches is discussed.
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