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IEEE PES Innovative Smart Grid Technologies

作者： Nian Zhang Pradeep K. Behera Department of Electrical and Computer Engineering University of the District of Columbia Department of Civil and Mechanical Engineering University of the District of Columbia

ISBN: (纸本)9781457721588

In response to the growing concern over the use of fossil fuels, renewable energy industries have been significant economic drivers in many parts of the United States. In the recent years there is a strong growth in solar power generation industries that requires prediction of solar energy to develop highly efficient stand-alone photovoltaic systems as well as hybrid power systems. In order to accomplish the goal, we propose a predictive model that is based on recurrent neural networks trained with the Levenberg-Marquardt backpropagation learning algorithm to forecast the solar radiation using the past solar radiation and solar energy. This computational intelligence modeling tool explored the impact of solar radiation and solar energy in forecasting reliable long-run solar energy. Based on the excellent experimental results including the mean squared error analysis, error autocorrelation function analysis, regression analysis, and time series response, it demonstrated that the proposed neural network structure and the learning algorithm could be very useful in training the recurrent neural network for the solar radiation prediction.

关键词： Solar radiation prediction Time series prediction Neural networks backpropagation learning algorithm

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Intelligent controlled three-phase squirrel-cage induction generator system using wavelet fuzzy neural network for wind power

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IET RENEWABLE POWER GENERATION 2013年第5期7卷 552-564页

作者： Lin, Faa-Jeng Tan, Kuang-Hsiung Fang, Dun-Yi Lee, Yih-Der Natl Cent Univ Dept Elect Engn Chungli 320 Taiwan Natl Def Univ Chung Cheng Inst Technol Sch Def Sci Tao Yuan 335 Taiwan Inst Nucl Energy Res Engn Technol & Facil Operat Div Tao Yuan 335 Taiwan

An intelligent controlled three-phase squirrel-cage induction generator (SCIG) system for grid-connected wind power application using wavelet fuzzy neural network (WFNN) is proposed in this study. First, the indirect field-oriented mechanism is implemented for the control of the SCIG system. Then, an AC/DC power converter and a DC/AC power inverter are developed to convert the electric power generated by a three-phase SCIG from variable-voltage and variable-frequency to constant-voltage and constant-frequency. Moreover, the intelligent WFNN controller is proposed for both the AC/DC power converter and DC/AC power inverter to improve the transient and steady-state responses of the SCIG system at different operating conditions. Three online trained WFNNs using backpropagation learning algorithm are implemented as the tracking controllers for the DC-link voltage of the AC/DC power converter and the active power and reactive power outputs of the DC/AC power inverter. Furthermore, the network structure and the online learning algorithm of the WFNN are introduced in detail. Finally, some experimental results are provided to demonstrate the effectiveness of the proposed SCIG system for wind power.

关键词： AC-DC power convertors asynchronous generators backpropagation DC-AC power convertors electric current control fuzzy neural nets machine vector control neurocontrollers phase control power generation control power grids reactive power control wavelet transforms wind power plants Intelligent controlled three-phase squirrel-cage induction generator system wavelet fuzzy neural network SCIG grid-connected wind power application indirect field-oriented mechanism AC-DC power converter DC-AC power inverter electric power generation variable-voltage variable-frequency constant-voltage constant-frequency intelligent WFNN controller WFNN training backpropagation learning algorithm tracking controller DC-link voltage controller reactive power output active power output

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A PSO based integrated functional link net and interval type-2 fuzzy logic system for predicting stock market indices

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APPLIED SOFT COMPUTING 2012年第2期12卷 931-941页

作者： Chakravarty, S. Dash, P. K. Siksha OAnusandhan Univ Bhubaneswar Orissa India Reg Coll Management Autonomous Bhubaneswar Orissa India

This paper presents an integrated functional link interval type-2 fuzzy neural system (FLIT2FNS) for predicting the stock market indices. The hybrid model uses a TSK (Takagi-Sugano-Kang) type fuzzy rule base that employs type-2 fuzzy sets in the antecedent parts and the outputs from the Functional Link Artificial Neural Network (FLANN) in the consequent parts. Two other approaches, namely the integrated FLANN and type-1 fuzzy logic system and Local Linear Wavelet Neural Network (LLWNN) are also presented for a comparative study. backpropagation and particle swarm optimization (PSO) learning algorithms have been used independently to optimize the parameters of all the forecasting models. To test the model performance, three well known stock market indices like the Standard's & Poor's 500 (S&P 500), Bombay stock exchange (BSE), and Dow Jones industrial average (DJIA) are used. The mean absolute percentage error (MAPE) and root mean square error (RMSE) are used to find out the performance of all the three models. Finally, it is observed that out of three methods, FLIT2FNS performs the best irrespective of the time horizons spanning from 1 day to 1 month. (C) 2011 Elsevier B. V. All rights reserved.

关键词： Functional Link Artificial Neural Network Fuzzy logic system Interval Type-2FLS backpropagation learning algorithm Particle swarm optimization

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Multi-computer neural network architecture

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ELECTRONICS LETTERS 1999年第16期35卷 1350-1352页

作者： Howlett, RJ Walters, SD Univ Brighton Engn Res Ctr Brighton BN2 4GJ E Sussex England

A novel neural network architecture is presented which allows the efficient execution of the back-propagation learning algorithm on a multicomputer system which has a low communications bandwidth.

关键词： communication bandwidth class-distributed neural network architecture multi-computer system neural net architecture Neural computing techniques backpropagation backpropagation learning algorithm Parallel architecture

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Artificial neural network approach for a class of fractional ordinary differential equation

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NEURAL COMPUTING & APPLICATIONS 2017年第4期28卷 765-773页

作者： Jafarian, Ahmad Mokhtarpour, Masoumeh Baleanu, Dumitru Islamic Azad Univ Urmia Branch Dept Math Orumiyeh Iran Cankaya Univ Dept Math Eskisehir Yolu 29 Km TR-06810 Ankara Turkey Inst Space Sci Bucharest Romania

The essential characteristic of artificial neural networks which against the logistic traditional systems is a data-based approach and has led a number of higher education scholars to investigate its efficacy, during the past few decades. The aim of this paper was concerned with the application of neural networks to approximate series solutions of a class of initial value ordinary differential equations of fractional orders, over a bounded domain. The proposed technique uses a suitable truncated power series of the solution function and transforms the original differential equation in a minimization problem. Then, the minimization problem is solved using an accurate neural network model to compute the parameters with high accuracy. Numerical results are given to validate the iterative method.

关键词： Feed-forward neural network Fractional differential equation Approximate solution backpropagation learning algorithm

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Polynomial Based Functional Link Artificial Recurrent Neural Network adaptive System for predicting Indian Stocks

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2015年第6期8卷 1004-1016页

作者： Bebarta, D. K. Biswal, Birendra Dash, P. K. GMR Inst Technol Rajam AP India Siksha O Anusandhan Univ Bhubaneswar Odisha India

A low complexity Polynomial Functional link Artificial Recurrent Neural Network (PFLARNN) has been proposed for the prediction of financial time series data. Although different types of polynomial functions have been used for low complexity neural network architectures earlier for stock market prediction, a comparative study is needed to choose the optimal combinations of the nonlinear functions for a reasonably accurate forecast. Further a recurrent version of the Functional link neural network is used to model more accurately a chaotic time series like stock market indices with a lesser number of nonlinear basis functions. The proposed PFLARNN model when trained with the well known gradient descent algorithm produces reasonable accuracy with a choice of range of weight parameters of the network. However, to improve the accuracy of the forecast further, the weight parameters of the recurrent functional neural network are optimized using an evolutionary learning algorithm like the differential evolution (DE). A comparison with other well known neural architectures shows that the proposed low complexity neural model can provide significant prediction accuracy for one day advance and speed of convergence using the International Business Machines Corp. (IBM) stock market indices.

关键词： PFLARNN Polynomial functions backpropagation learning algorithm differential evolution IBM stock indices MAPE AMAPE

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PREDICTION OF HYPERVELOCITY ORBITAL DEBRIS IMPACT DAMAGE TO THE SPACE STATION BY NEURAL NETWORKS

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MATHEMATICAL AND COMPUTER MODELLING 1995年第1-2期21卷 229-242页

作者： GULEYUPOGLU, S SMITH, RE Department of Engineering Science and Mechanics The University of Alabama Box 870278 Tuscaloosa AL 35487 U.S.A.

Prediction of damage to orbiting space craft due to collisions with hypervelocity space debris is an important issue in the design of Space Station Freedom. Space station wall structures ace designed to absorb impact energy during a collision. A proposed wall structure consists of a multilayer insulation (MLI) directly covering the pressure wall, and a bumper layer placed 100 mm from the pressure wall. In experiments at The Marshall Space Flight Center, 2.5-12.7 mm projectiles have been fired at this wall structure at speeds of 2-8 km/s. In this paper, three-layer backpropagation networks are trained with two sets of impact damage data. The input parameters for training are pressure wall thickness, bumper plate thickness, projectile diameter, impact angle, and the projectile velocity. Output from the network consists of hole dimensions for the bumper and the pressure wall in the minor and major axis directions, and damage to the MLI. To evaluate network generalization, networks are tested with experimental data points that are not used for training. Network performance is compared with that of other damage prediction methods. Network determination of qualitative damage estimation is suggested as a new direction for research. Preliminary testing of qualitative prediction of pressure wall damage is presented. The results are promising, and suggest several areas for further study.

关键词： NONLINEAR SYSTEM MODELING AEROSPACE backpropagation learning algorithm DAMAGE ESTIMATION NEURAL NETWORK APPLICATIONS

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Modelling hybrid and backpropagation adaptive neuro-fuzzy inference systems for flood forecasting

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NATURAL HAZARDS 2021年第1期108卷 519-566页

作者： Tabbussum, Ruhhee Dar, Abdul Qayoom Natl Inst Technol Srinagar Dept Civil Engn Srinagar 190006 Jammu & Kashmir India

The ability of the adaptive neuro-fuzzy inference algorithm architecture to simulate floods is explored in this research. The development of models for flood forecasting has been centered on two adaptive neuro-fuzzy inference (ANFIS) algorithms. The Takagi-Sugeno fuzzy inference systems (FIS) generated through subtracted clustering were trained using hybrid and backpropagation training algorithms. Multiple statistical performance evaluators were used to assess the performability of the established models. The validity and predictive power of the models are evaluated by estimating a flood occurrence in the study area. In designing the models, a total of 12 inputs were employed. The best performability was found for the ANFIS model created utilizing a hybrid training algorithm with mean square error (MSE) of 0.00034, co-efficient of correlation (R-2) of 97.066%, root mean square error (RMSE) of 0.018, Nash-Sutcliffe model efficiency (NSE) of 0.968, mean absolute error (MAE) of 0.0073 and combined accuracy (CA) of 0.018, indicating the possible usage of exploiting the established model for prediction of floods.

关键词： Adaptive neuro-fuzzy inference system backpropagation learning algorithm Gaussian membership function Hybrid learning algorithm Subtractive clustering Takagi– Sugeno fuzzy inference system

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DATA-PROCESSING FOR MULTICHANNEL OPTICAL-RECORDING - ACTION-POTENTIAL DETECTION BY NEURAL NETWORK

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JOURNAL OF NEUROSCIENCE METHODS 1992年第1期43卷 23-33页

作者： YAMADA, S KAGE, H NAKASHIMA, M SHIONO, S MAEDA, M Biotechnology Department Mitsubishi Electric Corporation Hyogo Japan.

Using a neural network, we have developed a program for fast and precise detection of action potentials (AP) in raw multi-channel optical recording data. The AP detection was performed in two steps: first, peaks were detected in raw optical data, and, second, the peaks were classified by the neural network into APs, noise and undecided peaks. The network was optimized and trained by the backpropagation learning algorithm, employing some thousands of manually classified peaks. The performance of the optimized network was found to be not completely satisfactory, although it was better than the classification by template matching and nearest-neighbor rules. The addition of a signal-to-noise ratio (SNR) of a peak to the network classification improved the classification performance: in comparison with the manual classification results, 96% of manually classified APs were detected. The causes of classification errors were discussed. In spite of the fact that the program required a slight amount of human intervention for undecided peaks, the program could allow mostly automatic AP detection.

关键词： MULTISITE OPTICAL RECORDING DATA PROCESSING ACTION POTENTIAL DETECTION MULTILAYERED NEURAL NETWORK backpropagation learning algorithm APLYSIA GILL-WITHDRAWAL REFLEX

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A PC-BASED NEURAL NETWORK FOR ONLINE MEASUREMENT OF REGIONAL CEREBRAL BLOOD-FLOW

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COMPUTERS IN BIOLOGY AND MEDICINE 1992年第1-2期22卷 23-32页

作者： WU, FY YEN, KK FLORIDA INT UNIV DEPT ELECT & COMP ENGNMIAMIFL 33199

The measurement of regional cerebral blood flow has been used in diagnoses for many cerebral disorders. However, the complexity of data processing makes the technique only possible for off-line study. This paper explores a PC-based neural net technique for on-line determination of the blood flow rate. Experimental results justify the efficiency of the proposed methodology and raise the possibility of designing a portable system for time-critical applications.

关键词： REGIONAL CEREBRAL BLOOD FLOW XE-133 INHALATION METHOD FEEDFORWARD NEURAL NETWORK backpropagation learning algorithm

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