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Accelerated Learning for Restricted Boltzmann Machine with a Novel momentum algorithm

Chinese Journal of Electronics 2018年第3期27卷 483-487页

作者： GAO Xiaoguang LI Fei WAN Kaifang Northwestern Polytechnical University

We investigated two commonly used momentum algorithms, Classical momentum(CM) and Nesterov momentum(NM). We found that, when used in Restricted Boltzmann machine(RBM), they have two main problems: The first one is their performances are not obvious and not as good as expected. The second one is they may lose accelerating ability in the later stage of training process. Aiming at these two problems, we proposed the Weight momentum algorithm and evaluated our approach on four datasets. It has been demonstrated that our methods can achieve better performance under both reconstruction error and classification rate criterions.

关键词： Deep learning Restricted Boltzmann machine(RBM) momentum algorithm Weight momentum(WM)

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Decision Feedback Blind Equalization algorithm Based on momentum and Orthogonal Wavelet Packet Transform

Decision Feedback Blind Equalization Algorithm Based on Mome...

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5th International Conference on Wireless Communications, Networking and Mobile Computing

作者： Zhu Jie Guo Ye-cai Yang Chao Huainan Normal Univ Dept Phys & Elect Informat Huainan 232001 Peoples R China Nanjing Univ Informat Sci & Technol Coll Elect & Informat Engn Nanjing 210044 Peoples R China Anhui Univ Sci & Technol Huainan 232001 Anhui Peoples R China

ISBN: (纸本)9781424436927

Towards the nonlinear underwater acoustic channels with the severe linear distortion, based on the fast de-correlation characteristic of wavelet packet transform, a decision feedback blind equalization algorithm based on momentum and Orthogonal Wavelet Packet Transform (MWPT-DFE) is proposed, which is derived by revising the iteration equation of forward weight vector for decision feedback structure blind equalizer. Comparing with normal decision feedback blind equalization algorithm (DFE) and the decision feedback blind equalization algorithm based on orthogonal wavelet Packet transform (WPT-DFE), the new algorithm has fast convergence, good performance for tracking and smaller MSE. The efficiency of the proposed algorithm is proved by computer simulation in underwater acoustic channels.

关键词： blind equalization underwater acoustic channels decision feedback orthogonal wavelet packet transform momentum algorithm

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Compressive Sensing Reconstruction algorithm Based on Heavy-ball momentum

Compressive Sensing Reconstruction Algorithm Based on Heavy-...

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27th International Symposium on Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM)

作者： Long, Sheng Wang, Xianghan Li, Shuohao Ding, Zhaoyun Zhang, Jun Natl Univ Def Technol Lab Big Data & Decis Changsha Peoples R China Natl Univ Def Technol Sci & Technol Informat Syst Engn Lab Changsha Peoples R China

ISBN: (纸本)9798350387605;9798350387599

Nowadays, the digital world shows the development trend of high-dimensional mass, and most data can be structured or sparsely represented, so it is convenient to use Compressive Sensing (CS) to reduce the storage and transmission costs of big data. The key data reconstruction process in CS is usually formalized as a minimization problem, but it is difficult for existing algorithms to achieve high data reconstruction speed and accuracy at the same time. Heavy-ball momentum algorithm is the most representative acceleration algorithm in the field of optimization. In this paper, it is introduced into the reconstruction process of CS, and the convergence of the momentum algorithm to decompress the CS reconstruction optimization problem is guaranteed through theoretical analysis. The results of comparative experiments also show that the momentum algorithm can reconstruct the image with higher quality and consume the least computing time.

关键词： Compressive Sensing strongly convex optimization momentum algorithm image reconstruction

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Prediction model of agricultural water quality based on optimized logistic regression algorithm

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EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING 2023年第1期2023卷 21页

作者： Gai, RongLi Zhang, Hao Dalian Univ Dept Informat Engn Dalian Peoples R China

Aiming at the problem that the linear regression of the traditional linear water quality prediction model is not robust enough and the prediction accuracy cannot be guaranteed in the presence of interference, this paper proposes an agricultural water quality prediction model based on the momentum algorithm to optimize the logistic regression algorithm (LRM algorithm). The model uses the momentum algorithm to optimize the logistic regression algorithm to quickly adjust the misclassified samples. When the object encounters a local optimum in the process of falling, the introduction of momentum makes it easy for the next update to jump out of the local optimum with the help of the last large gradient. In this paper, the performance of the proposed model is evaluated on 4 real data sets. The experimental results show that the LRM algorithm proposed in this paper improves the prediction accuracy of the existing algorithm by an average of 1.11 percentage *** with KNN and other traditional prediction algorithms, LRM not only speeds up the convergence rate of the algorithm, but also reduces the steady-state error and improves the prediction accuracy of water quality, suitable for data mining of complex water quality data, The experiment verifies the feasibility of this method in predicting the actual agricultural water quality and even in predicting and warning the residents drinking water.

关键词： momentum algorithm Logistic regression Water quality prediction

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Online Blind Complex Source Separation algorithm with In-phase and Quadrature Component Joint Processing 7

Online Blind Complex Source Separation Algorithm with In-pha...

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7th International Conference on Intelligent Systems, Modelling and Simulation (ISMS)

作者： Yang, Hua Zhang, Hang Li, Jiong Yang, Liu PLA Univ Sci & Technol Coll Commun Engn Nanjing 210007 Jiangsu Peoples R China

ISBN: (纸本)9781509006656

Most of existing blind source separation (BSS) algorithms are suitable for real signals but difficult for complex signals, while transmission signals usually belong to complex domain in actual communication systems. This paper proposes a new online algorithm for blind complex source separation (BCSS). The proposed algorithm process in-phase and quadrature component of communication signal jointly to realize BCSS based on momentum EASI algorithm. Simulation results show that the proposed algorithm separates complex sources effectively. When applied to separate complex communication signal and jamming signals, the new algorithm can increases the anti-jamming margin of communication system greatly, and both of the symbol error ratio (SER) and the bit error ratio (BER) of communication signal is close to the theoretical bound.

关键词： Communication Anti-jamming Blind Complex Source Separation Blind Separation of Communication Signal and Jamming EASI momentum algorithm

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Gradient Optimized Blind Sources Separation algorithm

Gradient Optimized Blind Sources Separation Algorithm

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7th IEEE International Conference on Wireless Communications and Signal Processing (WCSP)

作者： Yang, Hua Zhang, Hang Yang, Liu PLA Univ Sci & Technol Coll Commun Engn Nanjing 210007 Jiangsu Peoples R China

ISBN: (纸本)9781467376877

In this paper, a new gradient optimized blind source separation algorithm (GOA) which aims at improving the convergence performance is proposed. This algorithm modifies the gradient of cost function to make the iteration process of separation matrix closer to its change pattern. To be more specific, by adding the difference value between current time gradient and previous time gradient to the adaptive iterations of separation matrix, the proposed algorithm effectively improves convergence rate. Simulation experiment results show that the GOA has faster convergence rate when compared with the traditional momentum EASI algorithm. In the small step size conditions, the advantage of GOA is even more obvious.

关键词： Blind Source Separation Gradient Optimizing EASI momentum algorithm

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An Ultra-Short-Term and Short-Term Wind Power Forecasting Approach Based on Optimized Artificial Neural Network with Time Series Reconstruction 4

An Ultra-Short-Term and Short-Term Wind Power Forecasting Ap...

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4th International Conference on Smart Power and Internet Energy Systems (SPIES)

作者： Zha, Lihan Jiang, DongXiang Tsinghua Univ WeiYang Coll Beijing Peoples R China Tsinghua Univ Dept Energy & Power Engn Beijing Peoples R China

ISBN: (纸本)9781665489577

Wind power forecasting (WPF) is crucial for grid dispatch and effective collection of wind power. In recent years, deep learning such as multi-layer perception (MLP) has been widely adopted in WPF. This paper proposes a novel ultra-short-term and short-term WPF approach based on optimized artificial neural network (ANN) with time series reconstruction (TSR) method. Z-scored method is adopted to preprocess the dataset measured from a wind farm. TSR is proposed and used to generate model inputs. An optimized ANN-based model with MLP architecture is introduced, and momentum algorithm and decaying learning rate are proposed for model optimization. Repeated trainings are performed to obtain the optimal model. The proposed approach is validated on the test case of a wind farm in China. The results obtained prove that the optimized ANN-based model and TSR can both effectively improve the accuracy of ultra-short-term and short-term WPF.

关键词： artificial neural network multi-layer perception time series reconstruction momentum algorithm wind power forecasting

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On the convergence speed of AMSGRAD and beyond 31

On the convergence speed of AMSGRAD and beyond

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31st IEEE International Conference on Tools with Artificial Intelligence (ICTAI)

作者： Tan, Tao Yin, Shiqun Liu, Kunling Wan, Man Southwest Univ Fac Comp & Informat Sci Chongqing 400715 Peoples R China

ISBN: (纸本)9781728137988

In ICLR's (2018) best paper "On the Convergence of Adam and Beyond", the author points out the shortcomings in Adam's convergence proof, proposes an AMSGRAD algorithm that can guarantee convergence as the number of iterations increases. However, through some comparative experiments, this paper finds that there are two problems in the convergence process of AMSGRAD algorithm. Firstly, the AMSGRAD algorithm is easy to oscillate;Secondly, the AMSGRAD algorithm converges slowly. After analysis, the above two problems can be solved by the following ways. When g(t-1) g(t) > 0, this paper adds the momentum term in momentum algorithm to the AMSGRAD algorithm to accelerate convergence. When g(t-1) g(t) <= 0, this paper use SGD algorithm instead of AMSGRAD algorithm to update the model weights. In order to eliminate some negative effects of the previous parameter gradient on the current parameter gradient and reduce the oscillation amplitude of the objective function, the first-order and second-order moment estimations of the parameter gradient are recalculated when g(t-1) g(t) <= 0. Therefore, this paper proposes the ACADG algorithm, which not only can improve the convergence speed, suppress the oscillation amplitude of the objective function, but also can improve the accuracy of training and test data sets.

关键词： AMSGRAD algorithm momentum algorithm oscillation amplitude ACADG algorithm convergence speed

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Spectrum Prediction based on Improved-Back-Propagation Neural Networks 11

Spectrum Prediction based on Improved-Back-Propagation Neura...

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11th International Conference on Natural Computation (ICNC) / 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Bai, Suya Zhou, Xin Xu, Fanjiang Chinese Acad Sci Inst Software Sci & Technol Integrated Informat Syst Lab Beijing Peoples R China

ISBN: (纸本)9781467376792

Spectrum prediction in the cognitive radio system attracts more and more attention. It can predict future spectrum holes to save energy of spectrum sensing and to improve the efficiency of spectrum access. The current research on spectrum prediction is to use the prediction model such as back propagation (BP) neural network to predict. However, the performance of conventional spectrum prediction is not satisfied to meet the real system for its using inaccurate spectrum states and defects of the BP neural network. Therefore, we propose a spectrum prediction based on improved-BP neural networks. In the proposed model, the channel power values information instead of the channel states are used as the inputs of the spectrum prediction, the BP neural network optimized by the genetic algorithm and momentum algorithm is utilized in the prediction process, and the threshold interval is applied to determine predicted channel states. Our experimental results demonstrate that the predictive accuracy of the proposed spectrum prediction based on the improved-BP neural network is higher than spectrum prediction based on conventional BP neural network.

关键词： spectrum prediction BP neural network genetic algorithm momentum algorithm threshold interval

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Design and Implementation of nonlinear neural network controller based on simulator model in Damavand Tokamak

Design and Implementation of nonlinear neural network contro...

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24th Chinese Control and Decision Conference (CCDC)

作者： Tolouei, H. Kouhi, A. Rasouli, H. Rasouli, C. KN Toosi Univ Technol Control Dept Fac Elect Engn Adv Proc Automat & Control Lab APAC Tehran Iran Plasma Phys & Nuclear Fus Inst Inst Nucl Sci & Technol AEOI Tehran Iran

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.

关键词： Tokamak Actuator circuits nonlinear neural network controller Gradient descent algorithm momentum algorithm

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