To control the robot and track the designed trajectory with uncertain disturbances in a specified precision range, an adaptive fuzzy control scheme for the robot arm manipulator is discussed. The controller output err...
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To control the robot and track the designed trajectory with uncertain disturbances in a specified precision range, an adaptive fuzzy control scheme for the robot arm manipulator is discussed. The controller output error method （COEM） is used to design the adaptive fuzzy controller. A few or all of the parameters of the controller are adjusted by using the gradient descent algorithm to minimize the output error. COEM is adopted in the adaptive control system for the robot arm manipulator with 5-DOF. Simulation results show the effectiveness of the method and the real time adjustment of the parameters.
A local linear wavelet neural network (LLWNN) is presented in this paper. The difference of the network with conventional wavelet neural network (WNN) is that the connection weights between the hidden layer and output...
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A local linear wavelet neural network (LLWNN) is presented in this paper. The difference of the network with conventional wavelet neural network (WNN) is that the connection weights between the hidden layer and output layer of conventional WNN are replaced by a local linear model. A hybrid training algorithm of particle swarm optimization (PSO) with diversity learning and gradientdescent method is introduced for training the LLWNN. Simulation results for the prediction of time-series show the feasibility and effectiveness of the proposed method. (c) 2005 Elsevier B.V. All rights reserved.
This paper presents an intelligent methodology for diagnosing incipient faults in rotating machinery. In this fault diagnosis system, wavelet neural network techniques are used in combination with a new evolutionary l...
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This paper presents an intelligent methodology for diagnosing incipient faults in rotating machinery. In this fault diagnosis system, wavelet neural network techniques are used in combination with a new evolutionary learning algorithm. This new evolutionary learning algorithm is based on a hybrid of the constriction factor approach for particle swarm optimization (PSO) technique and the gradientdescent (GD) technique, and is thus called HGDPSO. The HGDPSO is developed in such a way that a constriction factor approach for particle swarm optimization (CFA for PSO) is applied as a based level search, which can give a good direction to the optimal global region, and a local search gradientdescent (GD) algorithm is used as a fine tuning to determine the optimal solution at the final. The effectiveness of the HGDPSO based WNN is demonstrated through the classification of the fault signals in rotating machinery. The simulated results show its feasibility and validity. (C) 2005 Elsevier Inc. All rights reserved.
Reasonable bit error rate performance requires perfect channel state information （CSI） in traditional turbo equalization （TE）, which is hard to obtain in practice. Soft and hard iterative algorithms have been deve...
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Reasonable bit error rate performance requires perfect channel state information （CSI） in traditional turbo equalization （TE）, which is hard to obtain in practice. Soft and hard iterative algorithms have been developed to address the channel estimation problem with the performance of the soft iteratwe channel estimate based on the recursive least square algorithm. This paper presents an analysis of the performance of hard iterative channel estimation （HICE） based on the least mean square algorithm. The analysis uses a cost function with the hard decision on the TE output. An iterative channel correction （ICC） algorithm based on the gradient descent algorithm is used to iteratively minimize the cost function. The simulation results agree with the theoretical lower bound for the mean square error （MSE） of the estimated channels. Simulations show that, given an imperfect CSI with an MSE below the upper bound, the linear minimum mean squared error TE （LMMSE-TE） using the ICC has only small performance degradation compared to that with a perfect CSI, while the traditional LMMSE-TE suffers from severe error floor effect even with more iterations.
The major aim of this study was to model the effect of two causal factors, i.e. coating weight gain and amount of pectin-chitosan in the coating solution on the in vitro release profile of theophylline for bimodal dru...
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The major aim of this study was to model the effect of two causal factors, i.e. coating weight gain and amount of pectin-chitosan in the coating solution on the in vitro release profile of theophylline for bimodal drug delivery. Artificial neural network (ANN) as a multilayer perceptron feedforward network was incorporated for developing a predictive model of the formulations. Five different training algorithms belonging to three classes: gradientdescent, quasi-Newton (Levenberg-Marquardt, LM) and genetic algorithm (GA) were used to train ANN containing a single hidden layer of four nodes. The next objective of the current study was to compare the performance of aforementioned algorithms with regard to predicting ability. The ANNs were trained with those algorithms using the available experimental data as the training set. The divergence of the RMSE between the output and target values of test set was monitored and used as a criterion to stop training. Two versions of gradientdescent backpropagation algorithms, i.e. incremental backpropagation (IBP) and batch backpropagation (BBP) outperformed the others. No significant differences were found between the predictive abilities of IBP and BBP, although, the convergence speed of BBP is three- to four-fold higher than IBP. Although, both gradientdescent backpropagation and LM methodologies gave comparable results for the data modeling, training of ANNs with genetic algorithm was erratic. The precision of predictive ability was measured for each training algorithm and their performances were in the order of: IBP, BBP > LM > QP (quick propagation) > GA. According to BBP-ANN implementation, an increase in coating levels and a decrease in the amount of pectin-chitosan generally retarded the drug release. Moreover, the latter causal factor namely the amount of pectin-chitosan played slightly more dominant role in determination of the dissolution profiles. (c) 2006 Elsevier B.V. All rights reserved.
In this study, a modified hybrid neural network with asymmetric basis functions is presented for feature extraction of spike and slow wave complexes in electroencephalography (EEG). Feature extraction process has a gr...
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In this study, a modified hybrid neural network with asymmetric basis functions is presented for feature extraction of spike and slow wave complexes in electroencephalography (EEG). Feature extraction process has a great importance in all pattern recognition and classification problems. A gradient descent algorithm, indeed a back propagation type, is adapted to the proposed artificial neural network. The performance of the proposed network is measured using a support vector machine classifier fed by features extracted using the proposed neural network. The results show that the proposed neural network model can effectively be used in pattern recognition tasks. In experiments, real EEG data are used.
A novel single neuron speed controller of permanent magnet synchronous motor drive is presented, which is on-line trained by a gradient descent algorithm based on direct model reference adaptive control (MRAC) and fie...
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ISBN:
(纸本)1424403316
A novel single neuron speed controller of permanent magnet synchronous motor drive is presented, which is on-line trained by a gradient descent algorithm based on direct model reference adaptive control (MRAC) and field-oriented control scheme. A new error function is used to ensure motor speed tracking better. The system of permanent magnet synchronous motor drive is proved to be stable. The controller Is very simple and easy to be achieved on a digital computer. The simulation results reveal that proposed method can ensure motor speed tracking quickly and precisely and have good performance of the load disturbance attenuation.
This article presents a new two-layer neural network model for predicting the optimum solution to linear programming problems. An energy function that transforms linear programming problem into a non-linear function i...
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This article presents a new two-layer neural network model for predicting the optimum solution to linear programming problems. An energy function that transforms linear programming problem into a non-linear function is developed from the objective and constraints. The learning rule, based on gradient descent algorithm, is employed to get the appropriate weight structure of the neural network. The network is tested with different examples including a transportation problem. The results are compared along with the available solutions.
In this paper, an efficient and practical hybrid model has been proposed for congestion management analysis for both real and reactive power transaction under deregulated fuzzy environment of power system. The propose...
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ISBN:
(纸本)0780382374
In this paper, an efficient and practical hybrid model has been proposed for congestion management analysis for both real and reactive power transaction under deregulated fuzzy environment of power system. The proposed hybrid model determines the optimal bilateral or multilateral transaction and their corresponding load curtailment in two stages. In the first stage classical gradientdescent optimal power flow algorithm has been used to determine the set of feasible curtailment strategies for different amount of real and reactive power transactions. Whereas in the second stage fuzzy decision opinion matrix has been used to select the optimal transaction strategy considering increase in private power transaction, reduction in percentage curtailment, and its corresponding change in per unit generation cost and hence profit as fuzzy variables. The performance of the proposed algorithm has been tested using modified IEEE 30 bus test system. The solutions so obtained are found to be quite encouraging and reliable refer to both utility and customers.
In this Letter, the gradient descent algorithm is proposed to search for the optimal synchronization between a drive and a response hyperchaotic systems that are coupled with a scalar signal. The energy corresponds to...
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In this Letter, the gradient descent algorithm is proposed to search for the optimal synchronization between a drive and a response hyperchaotic systems that are coupled with a scalar signal. The energy corresponds to the largest Lyapunov exponent of the response system is defined. Its convergence indicates that the corresponding Lyapunov exponent is always negative. (C) 1999 Elsevier Science B.V. All rights reserved.
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