Multi-Agent Dam Management Model Based on Improved Reinforcement Learning Technology

In order to achieve efficient management of the dam, the new algorithms such as reinforcement learning, Synergetic, Structural Risk Minimization and Particle Swarm Optimization are used to establish a Cooperative Wavelet Least Squares Support Vector Machine Model. To improve the convergence rate and make full use of knowledge and advice of mechanics and hydraulics of the dam, WLS-SVRM and WLS-SVCM models are used cooperatively. Before the training online, mapping provides training samples for WLS-SVCM. During the course of training online, the numerical simulation and WLS-SVCM will provide knowledge and advices for WLS-SVRM. Case study shows that the model can provide timely information of gate opening and management information of the dam so as to provide decision support for engineering management.

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Support Vector Machine and its Application Status

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.1030-1032.1814 ◽

2014 ◽

Vol 1030-1032 ◽

pp. 1814-1817

Author(s):

Lan Lan Kang ◽

Wen Liang Cao

Keyword(s):

Support Vector Machine ◽

Small Error ◽

Support Vector ◽

Risk Minimization ◽

Minimization Principle ◽

Training Samples ◽

Testing Error ◽

Machine Theory ◽

Learning Machine ◽

Limited Training Samples

Support vector machine is a beginning of the 1990s, based on statistical learning theory proposed new machine learning method, which structural risk minimization principle as the theoretical basis, by appropriately selecting a subset of functions and discriminant function in the subset, so the actual risk of learning machine to a minimum, to ensure that the limited training samples obtained through a small error classifier, an independent test set for testing error remains small. In this paper, support vector machine theory, algorithm, application status, etc. are discussed in detail.

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Online Music Style Recognition via Mobile Computing

International Journal of Mobile Computing and Multimedia Communications ◽

10.4018/ijmcmc.293748 ◽

2022 ◽

Vol 13 (2) ◽

pp. 0-0

Keyword(s):

Machine Learning ◽

Real Life ◽

Recognition System ◽

Waveform Analysis ◽

Support Vector ◽

Learning Technology ◽

Machine Model ◽

Model Decomposition ◽

Online Music ◽

Music Information

Music is a widely used data format in the explosion of Internet information. Automatically identifying the style of online music in the Internet is an important and hot topic in the field of music information retrieval and music production. Recently, automatic music style recognition has been used in many real life scenes. Due to the emerging of machine learning, it provides a good foundation for automatic music style recognition. This paper adopts machine learning technology to establish an automatic music style recognition system. First, the online music is process by waveform analysis to remove the noises. Second, the denoised music signals are represented as sample entropy features by using empirical model decomposition. Lastly, the extracted features are used to learn a relative margin support vector machine model to predict future music style. The experimental results demonstrate the effectiveness of the proposed framework.

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A SVM and SLIC Based Detection Method for Paddy Field Boundary Line

Sensors ◽

10.3390/s20092610 ◽

2020 ◽

Vol 20 (9) ◽

pp. 2610 ◽

Cited By ~ 1

Author(s):

Yanming Li ◽

Zijia Hong ◽

Daoqing Cai ◽

Yixiang Huang ◽

Liang Gong ◽

...

Keyword(s):

Support Vector Machine ◽

Texture Features ◽

Boundary Detection ◽

Segmentation Algorithm ◽

Support Vector ◽

Boundary Line ◽

Navigation Systems ◽

Machine Model ◽

Training Samples ◽

Time Requirements

Visual based route and boundary detection is a key technology in agricultural automatic navigation systems. The variable illumination and lack of training samples has a bad effect on visual route detection in unstructured farmland environments. In order to improve the robustness of the boundary detection under different illumination conditions, an image segmentation algorithm based on support vector machine was proposed. A superpixel segmentation algorithm was adopted to solve the lack of training samples for a support vector machine. A sufficient number of superpixel samples were selected for extraction of color and texture features, thus a 19-dimensional feature vector was formed. Then, the support vector machine model was trained and used to identify the paddy ridge field in the new picture. The recognition F1 score can reach 90.7%. Finally, Hough transform detection was used to extract the boundary of the ridge field. The total running time of the proposed algorithm is within 0.8 s and can meet the real-time requirements of agricultural machinery.

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The Research of Least Square Support Vector Machine Model and its Simplified Method

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.241-244.1719 ◽

2012 ◽

Vol 241-244 ◽

pp. 1719-1723

Author(s):

Wen Jie Zhao ◽

Tao Zhang

Keyword(s):

Support Vector Machine ◽

Support Vector Machine Model ◽

Least Square ◽

Simplified Model ◽

Support Vector ◽

Machine Model ◽

Training Samples ◽

Svm Model ◽

Simplified Method ◽

Simulation Results

A simplified structure of the least square support vector machine (LS-SVM) model is proposed in this paper. Under the premise that the accuracy of LS-SVM model is unchanged, a small amount of training samples are chosen, which further fit this model by LS-SVM modeling. Finally, a typical nonlinear problem is taken as example to test the performance of this simplified model and the simulation results show that this simplified method proposed in this paper is effective.

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Multi-Agent Deep Reinforcement Learning for Decentralized Cooperative Traffic Signal Control

CICTP 2020 ◽

10.1061/9780784483053.039 ◽

2020 ◽

Author(s):

Yang Zhao ◽

Jian-Ming Hu ◽

Ming-Yang Gao ◽

Zuo Zhang

Keyword(s):

Reinforcement Learning ◽

Traffic Signal ◽

Signal Control ◽

Traffic Signal Control ◽

Multi Agent

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Predicting Ink Transfer Rate of 3D Additive Printing Using EGBO Optimized Least Squares Support Vector Machine Model

Mathematical Problems in Engineering ◽

10.1155/2020/8642430 ◽

2020 ◽

Vol 2020 ◽

pp. 1-12

Author(s):

Shengpu Li ◽

Yize Sun

Keyword(s):

Support Vector Machine ◽

Prediction Model ◽

Least Squares ◽

Transfer Rate ◽

Coefficient Of Determination ◽

Percentage Error ◽

Support Vector ◽

Experimental Sample ◽

Machine Model ◽

Ink Transfer

Ink transfer rate (ITR) is a reference index to measure the quality of 3D additive printing. In this study, an ink transfer rate prediction model is proposed by applying the least squares support vector machine (LSSVM). In addition, enhanced garden balsam optimization (EGBO) is used for selection and optimization of hyperparameters that are embedded in the LSSVM model. 102 sets of experimental sample data have been collected from the production line to train and test the hybrid prediction model. Experimental results show that the coefficient of determination (R2) for the introduced model is equal to 0.8476, the root-mean-square error (RMSE) is 6.6 × 10 (−3), and the mean absolute percentage error (MAPE) is 1.6502 × 10 (−3) for the ink transfer rate of 3D additive printing.

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