In this study, nonlinear neural network controller will be developed to control plasma radial motion in Damavand Tokamak. It is essential to have a good model in order to design a proper controller for plasma radial m...
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ISBN:
(纸本)9781457720727
In this study, nonlinear neural network controller will be developed to control plasma radial motion in Damavand Tokamak. It is essential to have a good model in order to design a proper controller for plasma radial motion. To achieve this goal, actuator circuits are simulated and in consequence based on simulator model and simulated actuator circuits nonlinear neural network controller will be designed in Damavand Tokamak. Comparison between neural network controller output and PD controller output shows the efficiency of proposed approach.
With the development of the electronic technology, people have proposed higher requirements for the service quality on elevator, and the optimal elevator dispatching has developed a typical multi-objective optimal pro...
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ISBN:
(纸本)9783037852866
With the development of the electronic technology, people have proposed higher requirements for the service quality on elevator, and the optimal elevator dispatching has developed a typical multi-objective optimal process. This paper analyzes both the advantages and the disadvantages of artificial immune algorithm and gradient descent algorithm, optimizes artificial immune algorithm, then proposes a novel optimal hybrid algorithm;at the same time, uses this hybrid algorithm in the elevator group control system combined with Pareto solution set. Making a comparison between the hybrid algorithm and the standard artificial immune algorithm, it's clear that this hybrid algorithm has certain feasibility and superiority, and to some extent, has improved the overall performance and service quality of the elevator group control system. This paper has provided a new method and a new thought on determination of the multi-objective weighted values in the elevator group control system.
The neural networks have been widely applied to optimum calculation and solution of complicated problems. In particular, the evolutional learning neural network has better characteristic and higher precision than othe...
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ISBN:
(纸本)9781457721205
The neural networks have been widely applied to optimum calculation and solution of complicated problems. In particular, the evolutional learning neural network has better characteristic and higher precision than other neural networks. But the slow computational rate of evolutional learning and the local-optimum of the evolutional learning neural network seriously influence its application. In this paper, the fast algorithm combining the gradient descent algorithm with the evolutional learning algorithm can effectively solve above problems. This neural network has been extensively applied.
In this article, we present two distance-based sensor network localization algorithms. The location of the sensors is unknown initially and we can estimate the relative locations of sensors by using knowledge of inter...
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ISBN:
(纸本)9781457702518
In this article, we present two distance-based sensor network localization algorithms. The location of the sensors is unknown initially and we can estimate the relative locations of sensors by using knowledge of inter-sensor distance measurements. Together with the knowledge of the absolute locations of three or more sensors, we can also determine the locations of all the sensors in the wireless network. The proposed algorithms make use of gradientdescent to achieve excellent localization accuracy. The two gradient descent algorithms are iterative in nature and result is obtained when there is no further improvement on the accuracy. Simulation results have shown that the proposed algorithms have better performance than existing localization algorithms. A comparison of different methods is given in the paper.
In this paper, a novel gradientdescent learning algorithm based on Gaussian Mixture Model (GMM) applied on Radial Basis Function Neural Network (RBFNN) is proposed in order to approximate the functions which have hig...
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ISBN:
(纸本)9781467357135;9781467357128
In this paper, a novel gradientdescent learning algorithm based on Gaussian Mixture Model (GMM) applied on Radial Basis Function Neural Network (RBFNN) is proposed in order to approximate the functions which have high non-linear order. How we can choose the strategy of gradientdescent including learning coefficients selecting and really is it optimized to learn the same for all feature vectors?, are the challenges made us to think precisely on those. In this study, GMM estimates the probability density of the feature space and then the optimal learning rates can be evaluated proportional to these probabilities. These cause the neurons to learn correspondence with the feature distribution likelihoods. Considering robust satellite subset selection, Geometric Dilution of Precision (GDOP) factor is calculated for all subset of satellites and then the subset with lowest value is selected for positioning, but it is so non-linear and has computational burden to navigation systems. We use the proposed method to approximate it. The results on real GPS measurements demonstrate that it significantly track the non-linearity of GPS GDOP.
A recurrent neuro fuzzy network (RNFN) model-based multistep ahead predictive control strategy is proposed in this article. The fuzzy logic (FL) and neural networks (NN) are intelligent system approaches, and they com...
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A recurrent neuro fuzzy network (RNFN) model-based multistep ahead predictive control strategy is proposed in this article. The fuzzy logic (FL) and neural networks (NN) are intelligent system approaches, and they complement each other. Hybridization of FL and NN utilizes the concepts of human cognitive capabilities and biological systems, respectively. Dynamic processes necessitate past information about the process input/output variables. In order to store the information, a memory unit is introduced between the fuzzy inference layer and the fuzzification layer. This recurrent structure enhances the prediction capability;hence, this RNFN model can be used to develop the multistep ahead predictive controller. The objective function of model based controller (MPC) minimizes the future control moves. The gradientdescent (GD) algorithm is used to the optimize control moves. The proposed RNFN model is used to develop a model predictive controller. The performance of the RNFN-MPC is compared with that of a neuro fuzzy network (NFN)-based MPC for a laboratory scale quadruple tank process.
Over the past several decades, concerns have been raised over the possibility that the exposure to extremely low frequency electromagnetic fields from power lines may have harmful effects on human and living organisms...
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Over the past several decades, concerns have been raised over the possibility that the exposure to extremely low frequency electromagnetic fields from power lines may have harmful effects on human and living organisms. This work involved the computation of the magnetic field generated by 110 kV overhead power lines using a normalized radial basis function (NRBF) network. Training of the evolving NRBF network is achieved by using the data generated from the numerical simulation based on Charge Simulation method (CSM). Then, NRBF has been used to determine the magnetic field distribution in a new geometry differing from the geometries used for training. These test results show that proposed NRBF network can be used as useful tool to calculate the magnetic fields from power lines, alternative to the conventional methods. (c) 2011 Elsevier Ltd. All rights reserved.
The main focus of this paper is to introduce a new supervised learning algorithm for spiking neural networks. The learning algorithm minimizes the overall differences between spike times of target and test spike train...
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ISBN:
(纸本)9781424496365
The main focus of this paper is to introduce a new supervised learning algorithm for spiking neural networks. The learning algorithm minimizes the overall differences between spike times of target and test spike trains by utilizing a new quantitative similarity measure which has been defined in this work. The actual membrane potential of a post-synaptic neuron is adjusted at the time of spikes based on what has been measured from similarity measure in order to generate the desired membrane potential. Finally, by utilizing gradient descent algorithm, the parameters of the spiking neural network are tuned to generate the desired output membrane potential. The proposed algorithm was applied to tune the facilitation, depression, and synaptic weight constants of the Dynamic Synapses Neural Network - DSNN - for the aim of input-output functional mapping. The simulation results show that the system identification task converges to the global optimum. The rate-to-time coding simulation performs with more than 75 percent accuracy. The performance of both system identification and rate-to-time coding is due to adaptation of short and long term synaptic parameters which cannot be accomplished if only synaptic weight is adapted.
In recent years machine learning technologies have been applied to ranking, and a new research branch named "learning to rank" has emerged. Three types of learning-to-rank methods - pointwise, pairwise and l...
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ISBN:
(纸本)9781424496365
In recent years machine learning technologies have been applied to ranking, and a new research branch named "learning to rank" has emerged. Three types of learning-to-rank methods - pointwise, pairwise and listwise approaches - have been proposed. This paper is concerned with listwise approach. Currently structural support vector machine(SVM) and linear neural network have been utilized in listwise approach, but these methods only consider the content relevance of an object with respect to queries, they all ignore the relationships between objects. In this paper we study how to use relationships between objects to improve the performance of a ranking model. A novel ranking function is proposed, which combines the content relevance of documents with respect to queries and relation information between documents. Two types of loss functions are constructed as the targets for optimization. Then we utilize neural network and gradient descent algorithm as model and training algorithm to build ranking model. In the experiments, we compare the proposed methods with two conventional listwise approaches. Experimental results on OHSUMED dataset show that the proposed methods outperform the conventional methods.
Nonlinear models have recently shown interesting properties for spectral unmixing. This paper considers a generalized bilinear model recently introduced for unmixing hyperspectral images. Different algorithms are stud...
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ISBN:
(纸本)9781457710056
Nonlinear models have recently shown interesting properties for spectral unmixing. This paper considers a generalized bilinear model recently introduced for unmixing hyperspectral images. Different algorithms are studied to estimate the parameters of this bilinear model. The positivity and sum-to-one constraints for the abundances are ensured by the proposed algorithms. The performance of the resulting unmixing strategy is evaluated via simulations conducted on synthetic and real data.
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