In this paper, we investigate the anti-jamming channel selection problem in interference mitigation-based wireless networks. Due to the specific traffic demands, the number of active users is variable. Then, an anti-j...
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In this paper, we investigate the anti-jamming channel selection problem in interference mitigation-based wireless networks. Due to the specific traffic demands, the number of active users is variable. Then, an anti-jamming dynamic game is formulated, and then it is proved to be an exact potential game admitting at least one pure strategy Nash equilibrium(NE). Moreover, a distributed anti-jamming channel selection algorithm(DACSA) is proposed. Finally, the simulation results are conducted to show the performance of the proposed DACSA scheme.
In order to improve solving Support Vector Machine algorithm, an improved learning algorithm of the parallel SMO is proposed. According to this algorithm, the master CPU averagely distributes primitive training set to...
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In order to improve solving Support Vector Machine algorithm, an improved learning algorithm of the parallel SMO is proposed. According to this algorithm, the master CPU averagely distributes primitive training set to slave CPUs so that they can almost independently run serial SMO on their respective training set As it adopts the strategies of buffer and shrink, the speed of the parallel training algorithm is increased, which is showed in the experiments of parallel SMO based on the dataset of MNIST. The experiments indicate that the parallel SMO algorithm has good performance in solving large-scale SVM.
A hysteretic neural network is proposed based on the associative memory principle of Hopfield neural network. The hysteretic character make the neurons in the hysteretic neural network have better holding property to ...
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A hysteretic neural network is proposed based on the associative memory principle of Hopfield neural network. The hysteretic character make the neurons in the hysteretic neural network have better holding property to the original states, which decreases the possibility of changing the states mistakenly, and enhances the accuracy and the successful rate of associative memory. Furthermore, a learning algorithm for multi-values patterns associative memory is proposed based Hebb rules. The weight matrix is designed dynamically according to the sample patterns and input pattern. Using the learning algorithm, the hysteretic neural network can realize any multi-values patterns associative memory. The simulation results prove the validity of the algorithm.
We present a novel variational inequality model (VIM) to capture the complex real decision-making process in multi-tiered supply chain networks (MSCN) without strictly limiting the features of related functions. The V...
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In this study, a new neuro-fuzzy technique is applied to estimate the wake field distribution on propeller plane of ship. The wake distribution data of stern flow fields have been collected systematically by model tes...
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Over the past five years, emerging economies have consistently progressed toward achieving greater economic independence. The aviation industry has played a significant role in driving this progress. Accurately foreca...
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This paper surveys the artificial neural networks approach. Researchers believe that these networks have the wide range of applicability, they can treat complicated problems as well. The work described here discusses ...
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This paper surveys the artificial neural networks approach. Researchers believe that these networks have the wide range of applicability, they can treat complicated problems as well. The work described here discusses an efficient computational method that can treat complicated problems. The paper intends to introduce an efficient computational method which can be applied to approximate solution of the linear two-dimensional Fredholm integral equation of the second kind. For this aim, a perceptron model based on artificial neural networks is introduced. At first, the unknown bivariate function is replaced by a multilayer perceptron neural net and also a cost function to be minimized is defined. Then a famous learning technique, namely, the steepest descent method, is employed to adjust the parameters (the weights and biases) to optimize their behavior. The article also examines application of the method which turns to be so accurate and efficient. It concludes with a survey of an example in order to investigate the accuracy of the proposed method.
The food retailing market has reached a mature stage where companies need to be competitive if they are to survive. Customers are ever more demanding and retailers need to design and introduce new ways of learning abo...
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A learning-based control approach is presented for force servoing of a robot with vision in an unknown environment. Firstly, mapping relationships between image features of the servoing object and the joint angles of ...
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A learning-based control approach is presented for force servoing of a robot with vision in an unknown environment. Firstly, mapping relationships between image features of the servoing object and the joint angles of the robot are derived and learned by a neural network. Secondly, a learning controller based on the neural network is designed for the robot to trace the object. Thirdly, a discrete time impedance control law is obtained for the force servoing of the robot,the on-line learning algorithms for three neural networks are developed to adjust the impedance parameters of the robot in the unknown environment. Lastly, wiping experiments are carried out by using a 6 DOF industrial robot with a CCD camera and a force/torque sensor in its end effector, and the experimental results confirm the effectiveness of the approach.
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