In a cement factory, a rotary kiln is the most complex component and it plays a key role in the quality and quantity of the final product. This system involves complex nonlinear dynamic equations that have not been co...
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In a cement factory, a rotary kiln is the most complex component and it plays a key role in the quality and quantity of the final product. This system involves complex nonlinear dynamic equations that have not been completely worked out yet. In conventional modeling procedures, a large number of the involved parameters are crossed out and an approximation model is presented instead. Therefore, the performance of the obtained model is very important and an inaccurate model may cause many problems in the design of a controller. This study presents a Takagi-Sugeno (TS)-type fuzzy system called a wavelet projection fuzzy inference system (WPFIS) in which a dimension reduction section is used at the input stage of the fuzzy system. In order to clarify the structure of the extracted features, structural learning with forgetting (SLF) based on Minkowski norms is proposed. In addition, gradient descent (GD) was used as a training algorithm. The results show that the proposed method has higher performance in comparison with conventional models. The data collected from Saveh White Cement Company were used in our simulations.
This paper addresses optimal motion for general machines. Approximation for optimal motion needs a global path planning algorithm that precisely calculates the whole dynamics of a machine in a brief calculation. We pr...
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ISBN:
(纸本)9781457708398
This paper addresses optimal motion for general machines. Approximation for optimal motion needs a global path planning algorithm that precisely calculates the whole dynamics of a machine in a brief calculation. We propose a path planning algorithm that is composed of a path searching algorithm and a pruning algorithm. The pruning algorithm is based on our analysis for the resemblances of states. To confirm the precision, calculation cost, optimality, and applicability of the proposed algorithm, we conducted several shortest time path planning examinations for the dynamic models of double inverted pendulums. The precision to reach the goal state of the pendulums was better than other algorithms. The calculation was at least 58 times faster. There was a positive correlation between the optimality and the resolutions of the proposed algorithm. As a result of torque based feedback control simulation, we confirmed applicability of the proposed algorithm under noisy situation.
In this paper, an Efficient Adaptive Fuzzy Neural Network (EAFNN) model is proposed for electric load forecasting. The proposed approach is based on an ellipsoidal basis function (EBF) neural network, which is functio...
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ISBN:
(纸本)9783642132070
In this paper, an Efficient Adaptive Fuzzy Neural Network (EAFNN) model is proposed for electric load forecasting. The proposed approach is based on an ellipsoidal basis function (EBF) neural network, which is functionally equivalent to the TSK model-based fuzzy system. EAFNN uses the combined pruning algorithm where both Error Reduction Ratio (ERR) method and a modified Optimal Brain Surgeon (OBS) technology are used to remove the unneeded hidden units. It can not only reduce the complexity of the network but also accelerate the learning speed. The proposed EAFNN method is tested on the actual electrical load data from well-known EUNITE competition data. Results show the proposed approach provides the superior forecasting accuracy when applying in the real data.
Motivation: Identification of differentially expressed genes from microarray datasets is one of the most important analyses for microarray data mining. Popular algorithms such as statistical t-test rank genes based on...
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Motivation: Identification of differentially expressed genes from microarray datasets is one of the most important analyses for microarray data mining. Popular algorithms such as statistical t-test rank genes based on a single statistics. The false positive rate of these methods can be improved by considering other features of differentially expressed genes. Results: We proposed a pattern recognition strategy for identifying differentially expressed genes. Genes are mapped to a two dimension feature space composed of average difference of gene expression and average expression levels. A density based pruning algorithm (DB pruning) is developed to screen out potential differentially expressed genes usually located in the sparse boundary region. Biases of popular algorithms for identifying differentially expressed genes are visually characterized. Experiments on 17 datasets from Gene Omnibus Database (GEO) with experimentally verified differentially expressed genes showed that DB pruning can significantly improve the prediction accuracy of popular identification algorithms such as t-test, rank product, and fold change. Conclusions: Density based pruning of non-differentially expressed genes is an effective method for enhancing statistical testing based algorithms for identifying differentially expressed genes. It improves t-test, rank product, and fold change by 11% to 50% in the numbers of identified true differentially expressed genes. The source code of DB pruning is freely available on our website http://***/degprune
The pruning algorithms for sparse least squares support vector regression machine are common methods,and easily comprehensible,but the computational burden in the training phase is heavy due to the retraining in perfo...
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The pruning algorithms for sparse least squares support vector regression machine are common methods,and easily comprehensible,but the computational burden in the training phase is heavy due to the retraining in performing the pruning process,which is not favorable for their *** this end,an improved scheme is proposed to accelerate sparse least squares support vector regression machine.A major advantage of this new scheme is based on the iterative methodology,which uses the previous training results instead of retraining,and its feasibility is strictly verified ***,experiments on benchmark data sets corroborate a significant saving of the training time with the same number of support vectors and predictive accuracy compared with the original pruning algorithms,and this speedup scheme is also extended to classification problem.
In classic phase space reconstruction, the time lag is identical. In our research, the different time lags are found more effectively for teletraffic forecasting. In this paper, a method to determine the different tim...
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ISBN:
(纸本)9780769538532
In classic phase space reconstruction, the time lag is identical. In our research, the different time lags are found more effectively for teletraffic forecasting. In this paper, a method to determine the different time lags in phase space reconstruction is proposed. Simulation results show that the prediction is more accurate by using the different time lags in reconstruction phase space.
In classic phase space reconstruction,the time lag is *** our research,the different time lags are found more effectively for teletraffic *** this paper,a method to determine the different time lags in phase space rec...
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In classic phase space reconstruction,the time lag is *** our research,the different time lags are found more effectively for teletraffic *** this paper,a method to determine the different time lags in phase space reconstruction is *** results show that the prediction is more accurate by using the different time lags in reconstruction phase space.
Aimed at the great computing complexity of optimal brain surgeon (OBS) process, a pruning algorithm with penalty OBS process is presented. Compared with sensitive and regularized methods, the penalty OBS algorithm not...
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Aimed at the great computing complexity of optimal brain surgeon (OBS) process, a pruning algorithm with penalty OBS process is presented. Compared with sensitive and regularized methods, the penalty OBS algorithm not only avoids time-consuming defect and low pruning efficiency in OBS process, but also keeps higher generalization and pruning accuracy than Levenberg-Marquardt method.
Mechanical properties of transformation induced plasticity (TRIP)-aided multiphase steels are modeled by neural networks using two methods of reducing the network connectivity, viz. a pruning algorithm and a predator ...
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Mechanical properties of transformation induced plasticity (TRIP)-aided multiphase steels are modeled by neural networks using two methods of reducing the network connectivity, viz. a pruning algorithm and a predator prey algorithm, to gain understanding on the impact of steel composition and treatment. The pruning algorithm gradually reduces the complexity of the lower layer of connections, removing less significant connections. In the predator prey algorithm, a genetic algorithm based multi-objective optimization technique evolves neural networks on a Pareto front, simultaneously minimizing training error and network size. The results show that the techniques find parsimonious models and, furthermore, extract useful knowledge from the data.
Tikhonov regularized SVM is a kind of new SVM which can convert multi-class problems to be single optimized problems. Since SVM has some limitations in disposition of big data collection, this paper puts forward a new...
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ISBN:
(纸本)9781424421138
Tikhonov regularized SVM is a kind of new SVM which can convert multi-class problems to be single optimized problems. Since SVM has some limitations in disposition of big data collection, this paper puts forward a new reduction Tikhonov regularized SVM by utilizing pruning algorithm to gain reduction data collection. Meanwhile, the paper applies genetic algorithm to make automatic selection from the balance parameter and kernel function parameter of Tikhonov regularized SVM. The experiment proves this newly improved Tikhonov Regularized SVM is more advantageous for classifying precision and train rate.
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