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检索条件"主题词=Generalized regression neural network"

共 339 条记录，以下是61-70 订阅

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Pattern recognition based photovoltaic power forecast using generalized regression neural network

Pattern recognition based photovoltaic power forecast using ...

引用

Chinese Automation Congress (CAC)

作者： Liu, Jiao Wang, Lingxiao Guo, Xiaogang Yang, Qiang Yan, Wenjun Zhejiang Univ Coll Elect Engn Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9781538635247

In the past decade, solar energy occupies an increasing share of the power grid. The intermittent fluctuations characteristics of solar irradiance bring great difficulties and challenges to the power system management and dispatch which simultaneously makes photovoltaic power forecast the most important. For this reason, pattern recognition model for weather statuses is constructed, classification forecast approach is put forward after the comparison of different power forecast models based on the generalized regression neural network. In order to assure the reliability of the result, the cross validation which is used to obtain the optimal value of spread and normalization of data is introduced into the forecast model. The generalized regression neural network model implemented its performance for photovoltaic power forecast has been studied through a set of simulation experiments for different scenarios. The comparisons of numerical results demonstrate the effectiveness of the proposed model.

关键词： generalized regression neural network pattern recognition photovoltaic power forecast neural network

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Application of generalized regression neural network in Cloud Security Intrusion Detection

Application of Generalized Regression Neural Network in Clou...

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International Conference on Robots & Intelligent System (ICRIS)

作者： Gao, Feng Chongqing Univ Arts & Sci Dept Software Engn Chongqing Peoples R China

ISBN: (纸本)9781538612279

By using generalized regression neural network clustering analysis, effective clustering of five kinds of network intrusion behavior modes is carried out. First of all, intrusion data is divided into five categories by making use of fuzzy C means clustering algorithm. Then, the samples that are closet to the center of each class in the clustering results are taken as the clustering training samples of generalized neural network for the data training, and the results output by the training are the individual owned invasion category. The experimental results showed that the new algorithm has higher classification accuracy of network intrusion ways, which can provide more reliable data support for the prevention of the network intrusion.

关键词： generalized regression neural network cloud security intrusion detection

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Medium-Term Electric Energy Demand Forecasting Using generalized regression neural network 39th

Medium-Term Electric Energy Demand Forecasting Using General...

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39th International Conference on Information Systems Architecture and Technology (ISAT)

作者： Pelka, Pawel Dudek, Grzegorz Czestochowa Tech Univ Dept Elect Engn Al Armii Krajowej 17 PL-42200 Czestochowa Poland

ISBN: (纸本)9783319999968;9783319999951

Medium-term electric energy demand forecasting is becoming an essential tool for energy management, maintenance scheduling, power system planning and operation. In this work we propose generalized regression neural network as a model for monthly electricity demand forecasting. This is a memory-based, fast learned and easy tuned type of neural network which is able to generate forecasts for many subsequent time-points in the same time. Time series preprocessing applied in this study filters out a trend and unifies input and output variables. Output variables are encoded using coding variables describing the process. The coding variables are determined on historical data or predicted. In application examples the proposed model is applied to forecasting monthly energy demand for four European countries. The model performance is compared to performance of alternative models such as ARIMA, exponential smoothing, Nadaraya-Watson regression and neuro-fuzzy system. The results show high accuracy of the model and its competitiveness to other forecasting models.

关键词： generalized regression neural network Medium-term load forecasting Pattern-based forecasting

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A generalized regression neural network based on fuzzy means clustering and its application in system identification

A generalized regression neural network based on fuzzy means...

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International Symposium on Information Technology Convergence

作者： Zhao, Shi-jun Zhang, Jin-lei Li, Xun Song, Wei China Univ Petr Instruments Res Inst Dongying 257061 Shandong Peoples R China China Univ Petr Sch Comp Sci & Commun Engn Dongying 257061 Shandong Peoples R China Chinese Acad Sci Inst Automat Beijing 100080 Peoples R China Chonbuk Natl Univ Div Elect & Informat Engn Jeonju 561756 South Korea

ISBN: (纸本)9780769530451

A method to simply the generalized regression neural networks (GRNN) structure with a large numbers of training samples is proposed. The amount of Pattern Units is proportionate to the training samples. So in order to simplify the GRNN's structure, some of the representative samples should be selected to build the network. This paper takes the fuzzy means clustering algorithm. It combines with a similarity measurement, which is calculated between input elements, to find the best clustering centers. According to the simulation results, this strategy can largely simply the GRNN's structure and significantly improve the network's efficiency with just a tiny of loss in accuracy. The network structure built in this strategy can learn quickly, and is suitable to deal with the problems of nonlinear system identification.

关键词： generalized regression neural network fuzzy means clustering system identification

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An Empirical Research of Forecasting Model Based on the generalized regression neural network

An Empirical Research of Forecasting Model Based on the Gene...

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IEEE International Conference on Automation and Logistics

作者： Guo, Xinjiang Shi, Jinglun Xiao, Yao Jinan Univ Coll Informat Sci & Technol Guangzhou Guangdong Prov Peoples R China South China Univ Technol Sch Elect & Informat Engn Guangzhou Guangdong Peoples R China Jinan Univ Dept Food Sci & Engn Guangzhou Guangdong Peoples R China

ISBN: (纸本)9781424425020

In this paper, we study the theory of generalized regression neural networks, a kind of radial basis network that is often used for function approximation, and apply it for the forecasting of the Shanghai composite index of the Chinese stock market. The raw data consists of 4245 observations of daily closing values of the Shanghai Composite Index spanning the trading dates December 19, 1990 to April 25, 2008. Each group of the training set is composed of 130 observations of daily closing values of the Shanghai Composite Index. The neural network we established has four layers of neurons: the input layer, the radial basis layer, the special linear layer and the output layer. Each of the first three layers has 11 neurons and the output layer has one neuron. Five statistics of forecasting error, including ME, MAE, RMSE, MAPE, and SE, are used for the evaluation of the forecasting results. The simulation results show that the generalized regression neural network we constructed is able to forecast the daily closing price of the Shanghai Composite Index and the effectiveness and high performance are demonstrated by the simulation results and five statistics. Therefore the forecasting model based on the generalized regression neural network is able to result in good prediction and has research value to the reality.

关键词： generalized regression neural network Function Approximation Radial Functions

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INTERBAND PREDICTION OF HYPERSPECTRAL IMAGES USING generalized regression neural network

INTERBAND PREDICTION OF HYPERSPECTRAL IMAGES USING GENERALIZ...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Halder, Kalyan Kumar Paul, Manoranjan Charles Sturt Univ Sch Comp & Math Bathurst NSW 2795 Australia

ISBN: (纸本)9781538605608

Predicting upcoming bands of hyperspectral images is an important task in modern image compression algorithms. This paper proposes a new algorithm to predict the band-wise correlation of hyperspectral images based on a generalized regression neural network (GRNN). The proposed algorithm uses the intensity values of the previous bands to train the GRNN and approximates the correlation between them. The next band is then predicted using the trained network and the immediately previous band. This algorithm works on a pixel-by-pixel basis and does not involve any mathematical modeling or any previous knowledge of the images. The performance of the proposed algorithm is evaluated by applying it to several Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) reflectance datasets. Simulation results show that the proposed algorithm provides substantial accuracy in the prediction of upcoming bands.

关键词： generalized regression neural network hyperspectral image image coding remote sensing interband prediction

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Particle Swarm Optimization Based Optimal Reliability Design of Composite Electric Power System Using Non-sequential Monte Carlo Sampling and generalized regression neural network

Particle Swarm Optimization Based Optimal Reliability Design...

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4th International Conference on Swarm, Evolutionary, and Memetic Computing (SEMCCO)

作者： Bakkiyaraj, R. Ashok Kumarappan, Narayanan Annamalai Univ Dept Elect Engn Chidambaram 608002 India

ISBN: (纸本)9783319037530;9783319037523

This paper presents a new approach based on particle swarm optimization (PSO) for determining the optimal reliability parameters of composite system using non-sequential Monte Carlo Simulation (MCS) and generalized regression neural network (GRNN). The cost-benefit based design model has been formulated as an optimization problem of minimizing system interruption cost and component investment cost. Solution of this design model requires the analysis of several reliability levels which needs to evaluate EDNS index for those levels. Evaluation of EDNS in non-sequential MCS requires state adequacy analysis for several thousands of sampled states. In conventional approaches, a dc load flow based load curtailment minimization model is solved for analyzing the adequacy of each sampled state which requires large computational resources. This paper reduces the computational burden by applying GRNN for state adequacy analysis of the sampled states. The effectiveness of the proposed methodology is tested on the IEEE 14-bus system.

关键词： Composite electric power system Reliability planning Monte Carlo simulation generalized regression neural network Particle swarm optimization

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The Prediction of Petroleum Pipeline Data Based on Matrix Rotation-generalized regression neural network

The Prediction of Petroleum Pipeline Data Based on Matrix Ro...

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3rd Global Congress on Intelligent Systems (GCIS)

作者： Shen Yan Zhang Jing Sun Shuangshuang Harbin Engn Univ Coll Sci Harbin Peoples R China

ISBN: (纸本)9781467330725

This paper presents a new approach for petroleum pipeline data prediction. In order to obtain correlation coefficient matrix of variables of petroleum pipeline monitoring data monthly, a novel method of matrix rotation-generalized regression neural network for petroleum pipeline data prediction is proposed. The simulation analysis demonstrates that the model is not only more precise, but also more effective and feasible.

关键词： principal component analysis matrix rotation generalized regression neural network correlation coefficient matrix

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Recognition of License Plate Character Based on Wavelet Transform and generalized regression neural network

Recognition of License Plate Character Based on Wavelet Tran...

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24th Chinese Control and Decision Conference (CCDC)

作者： Wang Yutao Qin Tingting Tian Ruixia Yang Gang Northeastern University Shenyang China

ISBN: (纸本)9781457720727

Recognition methods of Chinese characters and similar characters are the main factors which affect the rate of license plate character recognition system. In this paper, a hybrid method based on wavelet transform and generalized regression neural network (GRNN) is proposed. For Chinese characters, a block projection with wavelet packet transform is adopted to extract the feature and a clustering algorithm is presented to reduce the dimension of feature vector. Moreover, the method of the characteristic regional recognition is introduced to recognition the similar characters. Besides, the clustering method based on wavelet transform is used to extract the feature of letter and digital characters. Furthermore, GRNN with powerful non-linear mapping capability and high fault-tolerant ability is designed as character classifier in the multi-network character recognition system. Finally, the result shows that the recognition rate of whole network could reach up to 91% and the classification algorithm has better robustness.

关键词： License plate character recognition Wavelet transform Wavelet packet transform generalized regression neural network

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Real-Time Optimal Trajectory Planning of Mobile Robot in Presence of Obstacle through generalized regression neural network 4

Real-Time Optimal Trajectory Planning of Mobile Robot in Pre...

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IEEE 4th International Conference on Knowledge-Based Engineering and Innovation (KBEI)

作者： Azimirad, Vahid Mortazavy, Vahid Sani, Mohammad Fattahi Univ Tabriz Fac Engn Emerging Technol Tabriz Iran

ISBN: (纸本)9781538626405

In this paper, a new approach for optimal real-time path planning of wheeled mobile robots based on generalized regression neural network (GRNN) and optimal control is presented. Optimal control is used to find an accurate mathematical solution for mobile robot with considering kinodynamic constraints. However, the drawback of this method is to be more time-consuming than standard real time systems. In this study, through the optimal control procedure, the best path is obtained, in terms of distance, input torque, and velocity. Then, the network is trained by these data, and optimal control is replaced by GRNN. It generates the path with acceptable reduction in cost function (at time of 0.3 s), which is suitable for real-time application of mobile robot. The simulation results demonstrate the ability of trained neural network in generating paths with less computational time cost in comparison with other methods which are merely generated by the optimal control procedure.

关键词： Mobile Robot Optimal Control generalized regression neural network Path Planning Obstacle Avoidance

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