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Deep Q-Networks (DQN) is one of the most well-known methods of deep reinforcement learning, which uses deep learning to approximate the action-value function. Solving numerous Deep reinforcement learning challenges such as moving targets problem and the correlation between samples are the main advantages of this model. Although there have been various extensions of DQN in recent years, they all use a similar method to DQN to overcome the problem of moving targets. Despite the advantagesarXiv:2008.06973v1 fatcat:l45ipnep7jfjzdmmmeawpog6pe
more »... d, synchronizing the network weight in a fixed step size, independent of the agent's behavior, may in some cases cause the loss of some properly learned networks. These lost networks may lead to states with more rewards, hence better samples stored in the replay memory for future training. In this paper, we address this problem from the DQN family and provide an adaptive approach for the synchronization of the neural weights used in DQN. In this method, the synchronization of weights is done based on the recent behavior of the agent, which is measured by a criterion at the end of the intervals. To test this method, we adjusted the DQN and rainbow methods with the proposed adaptive synchronization method. We compared these adjusted methods with their standard form on well-known games, which results confirm the quality of our synchronization methods.
The concepts of tensors with diagonal and circulant structure are defined and a framework is developed for the analysis of such tensors. It is shown a tensor of arbitrary order, which is circulant with respect to two particular modes, can be diagonalized in those modes by discrete Fourier transforms. This property can be used in the efficient solution of linear systems involving contractive products of tensors with circulant structure. Tensors with circulant structure occur in models for imagedoi:10.1016/j.laa.2010.03.032 fatcat:omov3gkqrrfvhi7l2d4s4mqjmm
more »... lurring with periodic boundary conditions. It is shown that the new framework can be applied to such problems.
Based on this property, it can be shown that noises and outliers within the data are transferred into these high oscillation parts (Rezghi, 2017) . ... This problem has been studied especially in image data in which vectorization destroys the spatial relations within an image (Ahmadi and Rezghi, 2020) . ...doi:10.3389/fninf.2020.581897 pmid:33328948 pmcid:PMC7734298 fatcat:bikmkhxvg5hc3kk3sxczx3xm7e
In this paper we introduce a new variant of L-curve to estimate the Tikhonov regularization parameter for the regularization of discrete ill-posed problems. This method uses the solution norm versus the regularization parameter. The numerical efficiency of this new method is also discussed by considering some test problems.doi:10.1016/j.cam.2009.05.016 fatcat:5ggdbmzaujgfbdi5ugadik6kem
We develop a detection model based on support vector machines (SVMs) and particle swarm optimization (PSO) for gene selection and tumor classification problems. The proposed model consists of two stages: first, the well-known minimum redundancy-maximum relevance (mRMR) method is applied to preselect genes that have the highest relevance with the target class and are maximally dissimilar to each other. Then, PSO is proposed to form a novel weighted SVM (WSVM) to classify samples. In this WSVM,doi:10.1155/2012/320698 pmid:22924059 pmcid:PMC3424529 fatcat:cwyyubttzfdbnb62ymswvy2bta
more »... O not only discards redundant genes, but also especially takes into account the degree of importance of each gene and assigns diverse weights to the different genes. We also use PSO to find appropriate kernel parameters since the choice of gene weights influences the optimal kernel parameters and vice versa. Experimental results show that the proposed mRMR-PSO-WSVM model achieves highest classification accuracy on two popular leukemia and colon gene expression datasets obtained from DNA microarrays. Therefore, we can conclude that our proposed method is very promising compared to the previously reported results.
In this paper, we propose a method to find the best Kronecker product approximation of the blurring operator which arises in three dimensional image restoration problems. We show that this problem can be reduced to a well known rank-1 approximation of the scaled three dimensional point spread function (PSF) array, which is much smaller. This approximation can be used as a preconditioner in solving image restoration problems with iterative methods. The comparison of the approximation by the newdoi:10.1137/130917260 fatcat:bymfktq7kvdahmjmzgm6topwn4
more »... caled PSF array and approximation by the original PSF array that is used in [J. G. Nagy and M. E. Kilmer, IEEE Trans. Image Process., 15 (2006), pp. 604-613], confirms the performance of the new proposed approximation.
Dimensionality reduction is a main step in the learning process which plays an essential role in many applications. The most popular methods in this field like SVD, PCA, and LDA, only can be applied to data with vector format. This means that for higher order data like matrices or more generally tensors, data should be fold to the vector format. So, in this approach, the spatial relations of features are not considered and also the probability of over-fitting is increased. Due to these issues,arXiv:1808.10632v3 fatcat:6f54int4jbclbofjbkz7m7mfdq
more »... n recent years some methods like Generalized low-rank approximation of matrices (GLRAM) and Multilinear PCA (MPCA) are proposed which deal with the data in their own format. So, in these methods, the spatial relationships of features are preserved and the probability of overfitting could be fallen. Also, their time and space complexities are less than vector-based ones. However, because of the fewer parameters, the search space in a multilinear approach is much smaller than the search space of the vector-based approach. To overcome this drawback of multilinear methods like GLRAM, we proposed a new method which is a general form of GLRAM and by preserving the merits of it have a larger search space. Experimental results confirm the quality of the proposed method. Also, applying this approach to the other multilinear dimensionality reduction methods like MPCA and MLDA is straightforward.
Fault diagnostics and prognostics are important topics both in practice and research. There is an intense pressure on industrial plants to continue reducing unscheduled downtime, performance degradation, and safety hazards, which requires detecting and recovering potential faults in its early stages. Intelligent fault diagnosis is a promising tool due to its ability to rapidly and efficiently processing collected signals and providing accurate diagnosis results. Although many studies havearXiv:1909.07801v5 fatcat:gjbiazgki5dr7pijdct6bjecxe
more »... ped machine leaning (M.L) and deep learning (D.L) algorithms for detecting the bearing fault, the results have generally been limited to relatively small train/test datasets and the input data has been manipulated (selective features used) to reach high accuracy. In this work, the raw data, collected from accelerometers (time-domain features) are taken as the input of a novel temporal sequence prediction algorithm to present an end-to-end method for fault detection. We used equivalent temporal sequences as the input of a novel Convolutional Long-Short-Term-Memory Recurrent Neural Network (CRNN) to detect the bearing fault with the highest accuracy in the shortest possible time. The method can reach the highest accuracy in the literature, to the best knowledge of authors of the present paper, voiding any sort of pre-processing or manipulation of the input data. Effectiveness and feasibility of the fault diagnosis method are validated by applying it to two commonly used benchmark real vibration datasets and comparing the result with the other intelligent fault diagnosis methods.
2020 6th Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS)
Fatemeh Gholamzadeh Maryam Amirmazlaghani Mansoor Rezghi Sara Bourbour Hamid Saeedi-Sourck Elahe Mangeli Masoomeh Momeni Amirhossein jafari Masoomeh Momeni Masoomeh Momeni Akram Heidarizadeh Nasser Masoumi ...doi:10.1109/icspis51611.2020.9349557 fatcat:uzs2jdklonbobjfcda3bihfry4