مقالات مرتبط با شبکه های عصبی

مقالات مرتبط با شبکه های عصبی

 

در ادامه لینک دانلود برخی از مقاله های مفید و کاربردی در زمینه شبکه های عصبی و مطالب مرتبط با آن را مشاهده می کنیم.

1. مقاله مرتبط با "استابلایزرهای سیستم توان شبکه عصبی مصنوعی در محیط سیستم توان چند دستگاهی"
در زیر چکیده این مقاله را مشاهده می کنید:

 

"Artificial Neural Network Power System Stabilizers in Multi-Machine Power System Environment"

 

Abstract - Effectiveness of an artificial neural network (ANN), functioning as a power system stabilizer (PSS), in damping multi-mode oscillations in a Rve-machine power system environment is investigated in this paper. Accelerating power of the generating unit is used as the input to the ANN PSS. The proposed ANN PSS using a multilayer neural network with error-backpropagation training method was trained over the full working range of the generating unit with a large variety of disturbances. The ANN was trained to memorize the reverse input/output mapping of the synchronous machine. Results show that the proposed ANN PSS can provide good damping for both local and inter-area modes of oscillations.
Keywords - Power System Stabilizer, Artificial Neural Network, Inverse Plant, Multi-layer Network, ETTOTB ackpropagation, Multi-machine. Multi-mode Oscillation.

لینک دانلود: 1.pdf

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 2. مقاله مرتبط با "آنالیز شبیه سازی شده آشوبی توسط مدل شبکه عصبی با آشوب گذرا"
در زیر چکیده این مقاله را مشاهده می کنید:

 

"Chaotic Simulated Annealing by a Neural Network Model with Transient Chaos"

 

Abstract--We propose a neural network model with transient chaos, or a transiently chaotic neural network ( TCNN) as an approximation method for combinatorial optimization problems, by introducing transiently chaotic dynamics into neural networks. Unlike conventional neural networks only with point attractors, the proposed neural network has richer and more flexible dynamics, so that it can be expected to have higher ability of searching for globally optimal or near-optimal solutions. A significant property of this model is that the chaotic neurodynamics is temporarily generated for searching and self-organizing, and eventually vanishes with autonomous decrease of a bifurcation parameter corresponding to the "'temperature" in the usual annealing process. Therefore, the neural network gradually approaches, through the transient chaos, to a dynamical structure similar to such conventional models as the Hopfield neural network which converges to a stable equilibrium point. Since the optimization process of the  ransiently chaotic neural network is similar to simulated annealing, not in a stochastic way but in a deterministically chaotic way, the new method is regarded as chaotic simulated annealing (CSA). Fundamental characteristics of the transiently chaotic neurodynamics are numerically investigated with examples of a single neuron model and the Traveling Salesman Problem (TSP). Moreover, a maintenance scheduling problem for generators in a practical power system is also analysed to verify practical efficiency of this new method.
Keywords--Neural network, Chaos, Transient chaos, Simulated annealing, TSP, Bifurcation, Combinatorial optimization problem, Modern heuristic, NP-hard.

 

لینک دانلود: 2.pdf

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3. مقاله مرتبط با "تشخیص و طبقه بندی اختلالات کیفیت توان با استفاده از تبدیل S و شبکه عصبی احتمالاتی"
در زیر چکیده این مقاله را مشاهده می کنید:

 

"Detection and Classification of Power Quality Disturbances Using S-Transform and Probabilistic Neural Network"

 

Abstract—This paper presents an S-Transform based probabilistic neural network (PNN) classifier for recognition of power quality (PQ) disturbances. The proposed method requires less number of features as compared to wavelet based approach for the identification of PQ events. The features extracted through the S-Transform are trained by a PNN for automatic classification of the PQ events. Since the proposed methodology can reduce the features of the disturbance signal to a great extent without losing its original property, less memory space and learning PNN time are required for classification. Eleven types of disturbances are considered for the classification problem. The simulation results reveal that the combination of S-Transform and PNN can effectively detect and classify different PQ events. The classification performance of PNN is compared with a feedforward multilayer (FFML) neural network (NN) and learning vector quantization (LVQ) NN. It is found that the classification performance of PNN is better than both FFML and LVQ.
Index Terms—Detection and classification of power quality disturbances, probabilistic neural network (PNN), S-transform.

لینک دانلود: 3.pdf

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4. مقاله مرتبط با "کاربردهای سیستم متخصص، منطق فازی و شبکه های عصبی در الکترونیک قدرت و کنترل حرکت"

در زیر چکیده این مقاله را مشاهده می کنید:

"Expert System, Fuzzy Logic, and Neural Network Applications in Power Electronics and Motion Control"


Artificial intelligence (AI) tools, such as expert system, fuzzy logic, and neural network are expected to usher a new era in power electronics and motion control in the coming decades. Although these technologies have advanced significantly in recent years and have found wide applications, they have hardly touched the power electronics and mackine drives area. The paper describes these Ai tools and their application in the area of power electronics and motion control. The body of the paper is subdivided into three sections which describe, respectively, the principles and applications of expert system, fuzzy logic, and neural network. The theoretical portion of each topic is of direct relevance to the application of power electronics. The example applications in the paper are taken from the published literature. Hopefully, the readers will be able to formulate new applications from these examples.

لینک دانلود: 4.pdf

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5. مقاله مرتبط با "پیش بینی توان و سرعت باد درازمدت با استفاده از مدل شبکه عصبی مکرر محلی"
در زیر چکیده این مقاله را مشاهده می کنید:

"Long-Term Wind Speed and Power Forecasting Using Local Recurrent Neural Network Models"


Abstract—This paper deals with the problem of long-term wind speed and power forecasting based on meteorological information. Hourly forecasts up to 72-h ahead are produced for a wind park on the Greek island of Crete. As inputs our models use the numerical forecasts of wind speed and direction provided by atmospheric modeling system SKIRON for four nearby positions up to 30 km away from the wind turbine cluster. Three types of local recurrent neural networks are employed as forecasting models, namely, the infinite impulse response multilayer perceptron (IIR-MLP), the local activation feedback multilayer network (LAF-MLN), and the diagonal recurrent neural network (RNN). These networks contain internal feedback paths, with the neuron connections implemented by means of IIR synaptic filters. Two novel and optimal on-line learning schemes are suggested for the update of the recurrent network’s weights based on the recursive prediction error algorithm. The methods assure continuous stability of the network durig the learning phase and exhibit improved performance compared to the conventional dynamic back propagation. Extensive experimentation is carried out where the three recurrent networks are additionally compared to two static models, a finite-impulse response NN (FIR-NN) and a conventional static-MLP network. Simulation results demonstrate that the recurrent models, trained by the suggested methods, outperform the static ones while they exhibit significant improvement over the persistent method.
Index Terms—Local recurrent neural networks, long-term wind power forecasting, nonlinear recursive least square learning, real time learning.

لینک دانلود: 5.pdf

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6. مقاله مرتبط با "کاربردهای شبکه عصبی در راه اندازی موتور و الکترونیک قدرت - مقدمه و چشم انداز"

در زیر چکیده این مقاله را مشاهده می کنید:

"Neural Network Applications in Power Electronics and Motor Drives—An Introduction and Perspective"


Abstract—Artificial intelligence (AI) techniques, particularly the neural networks, are recently having significant impact on power electronics and motor drives. Neural networks have created a new and advancing frontier in power electronics, which is already a complex and multidisciplinary technology that is going through dynamic evolution in the recent years. This paper gives a comprehensive introduction and perspective of neural network applications in the intelligent control and estimation for power electronics and motor drives area. The principal topologies of neural networks that are currently most relevant for applications in power electronics have been reviewed including the detailed description of their properties. Both feedforward and feedback or recurrent architectures have been covered in the description. The application examples that are discussed in this paper include nonlinear function generation, delayless filtering and waveform processing, feedback signal processing of vector drive, space vector PWM of two-level and multilevel inverters, adaptive flux vector estimation, and some of their combination for vector-controlled ac drive. Additional selected applications in the literature are included in the references. From the current trend of the technology, it appears that neural networks will find widespread applications in power electronics and motor drives in future.

Index Terms—Backpropagation network, induction motor drive, intelligent control and estimation, neural network, perceptron, recurrent network, space vector PWM.

لینک دانلود: 6.pdf

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7. مقاله مرتبط با "شبکه عصبی بر اساس تخمین تولید توان حداکثر از ماژول PV با استفاده از اطلاعات محیط"

در زیر چکیده این مقاله را مشاهده می کنید:

"Neural Network Based Estimation of Maximum Power Generation from PV Module Using Environmental Information"


Abstract: This paper presents an application of artificial neural network for the estimation of maximum power generation from the PV module. The output power from the PV module depends on the environmental factors such as irradiation, and cell temperature. For the operation planning of power systems, the prediction of the power generation is inevitable for the PV systems. For this purpose, irradiation, temperature, and wind velocity are utilized as the input information to the proposed neural network. The output is the predicted maximum power generation under the condition given by those environmental factors. Efficiency of the proposed estimation scheme is evaluated by using the actual data on daily, monthly, and yearly bases. The proposed method gives highly accurate prediction compared with the prediction by using the conventional multiple regression model.
Keywords: Artificial neural network, PV module, irradiation, temperature, wind velocity.

لینک دانلود: 7.pdf

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8. مقاله مرتبط با "شبکه عصبی مبتنی بر ردیابی نقطه حداکثر توان با استفاده از کنترلر فازی ..."

در زیر چکیده این مقاله را مشاهده می کنید:

"Neural Network Based Maximum Power Point Tracking of Coupled Inductor Interleaved Boost Converter Supplied PV System Using Fuzzy Controller"


Abstract—The photovoltaic (PV) generator exhibits a nonlinear – characteristic and its maximum power (MP) point varies with solar insolation. In this paper, a feedforward MP-point tracking scheme is developed for the coupled-inductor interleaved- boost-converter-fed PV system using a fuzzy controller. The proposed converter has lower switch current stress and improved efficiency over the noncoupled converter system. For a given solar insolation, the tracking algorithm changes the duty ratio of the converter such that the solar cell array voltage equals the voltage corresponding to the MP point. This is done by the feedforward loop, which generates an error signal by comparing the instantaneous array voltage and reference voltage corresponding to the MP point. Depending on the error and change of error signals, the fuzzy controller generates a control signal for the pulsewidth-modulation generator which in turn adjusts the duty ratio of the converter. The reference voltage corresponding to the MP point for the feedforward loop is obtained by an offline trained neural network. Experimental data are used for offline training of the neural network, which employs a backpropagation algorithm. The proposed peak power tracking effectiveness is demonstrated through simulation and experimental results. Tracking performance of the proposed controller is also compared with the conventional proportional-plus-integral-controller-based system. These studies reveal that the fuzzy controller results in better tracking performance.

Index Terms—Coupled-inductor interleaved boost converter, feedforward loop, fuzzy controller, maximum power (MP) operation, neural network, proportional plus integral controller, solar cell array (SCA).

لینک دانلود: 8.pdf

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9. مقاله مرتبط با "قدرت تست خطینگی شبکه های عصبی"

در زیر چکیده این مقاله را مشاهده می کنید:

"power of the neural network linearity test"


Abstract. Recently, a new linearity test for time series was introduced based on concepts from the theory of neural networks. Lee et al. have already studied the power properties of this test and they are further investigated here. They are compared by simulation with those of a Lagrange multiplier (LM) type test that we derive from the same single-hidden-layer neural network model. The auxiliary regression of our LM type test is a simple cubic ‘dual’ of the Volterra expansion of the original series, and the power of the test appears superior overall to that of the other test.

Keywords. Lagrange multiplier test; linearity testing; neural network; nonlinear time series; Volterra expansion.

لینک دانلود: 9.pdf

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10. مقاله مرتبط با "پیش بینی ترکیبات ردیابی در بیوگاز با استفاده از تولباکس شبکه عصبی در MATLAB"

در زیر چکیده این مقاله را مشاهده می کنید:

"Prediction of trace compounds in biogas from anaerobic digestion using the MATLAB Neural Network Toolbox"


Abstract - The outlook to apply the highly energetic biogas from anaerobic digestion into fuel cells will result in a significantly higher electrical efficiency and can contribute to an increase of renewable energy production. The practical bottleneck is the fuel cell poisoning caused by several gaseous trace compounds like hydrogen sulfide and ammonia. Hence artificial neural networks were developed to predict these trace compounds. The experiments concluded that ammonia in biogas can indeed be present up to 93 ppm. Hydrogen sulfide and ammonia concentrations in biogas were modelled successfully using the MATLAB Neural Network Toolbox. A script was developed which made it easy to search for the best neural network models’ input/output-parameters, settings and architectures. The models were predicting the trace compounds, even under dynamical conditions. The resulted determination coefficients (R2) were for hydrogen sulfide 0.91 and ammonia 0.83. Several model predictive control tool strategies were introduced which showed the potential to foresee, control, reduce or even avoid the presence of the trace compounds.

Keywords: Biogas; Prediction; Hydrogen sulfide; Ammonia; Modelling; Neural networks; MATLAB Neural Network Toolbox; Anaerobic digestion

لینک دانلود: 10.pdf

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11. مقاله مرتبط با "شبکه های عصبی مبتنی بر ویولت برای تشخیص و طبقه بندی اختلال توان"

در زیر چکیده این مقاله را مشاهده می کنید:

"Wavelet-Based Neural Network for Power Disturbance Recognition and Classification"


Abstract—In this paper, a prototype wavelet-based neuralnetwork classifier for recognizing power-quality disturbances is implemented and tested under various transient events. The discrete wavelet transform (DWT) technique is integrated with the probabilistic neural-network (PNN) model to construct the classifier. First, the multiresolution-analysis technique of DWT and the Parseval’s theorem are employed to extract the energy distribution features of the distorted signal at different resolution levels. Then, the PNN classifies these extracted features to identify the disturbance type according to the transient duration and the energy features. Since the proposed methodology can reduce a great quantity of the distorted signal features without losing its original property, less memory space and computing time are required. Various transient events tested, such as momentary interruption, capacitor switching, voltage sag/swell, harmonic distortion, and flicker show that the classifier can detect and classify different power disturbance types efficiently.

Index Terms—Parseval’s theorem, power quality, probabilistic neural network, wavelet transform.

لینک دانلود: 11.pdf

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12. مقاله مرتبط با "کاربرد شبکه های عصبی در ارزیابی انواع اتصال کوتاه، تشخیص نوع خطا و تعیین محل خطا در شبکه های قدرت"
در زیر چکیده این مقاله را مشاهده می کنید:

"کاربرد شبکه های عصبی در ارزیابی انواع اتصال کوتاه، تشخیص نوع خطا و تعیین محل خطا در شبکه های قدرت"

چکیده - در شبکه های قدرت، شناسایی و تعیین محل و نوع اتصال کوتاه در حداقل زمان ممکن و با دقت هر چه بیشتر، یکی از موضوعات جالب توجه محققین می باشد. به این وسیله از یک طرف، حفاظت هرچه بهتر شبکه های قدرت فراهم شده و از طرف دیگر باعث صرفه جویی در وقت و هزینه های مربوط به تعمیر و نگهداری سیستم خواهد شد. در این مقاله، برای این منظور از شبکه عصبی استفاده شده است. بطوریکه نوع اتصال کوتاه، اعم از SLG، LL، LLG و LLL مشخص میشود و سپس محل تقریبی آن تخمین زده میشود.

کلید واژه - اتصال کوتاه، حفاظت شبکه قدرت، شبکه های عصبی

لینک دانلود: 12.pdf

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13. مقاله مرتبط با "مدل سازی و پیش بینی مصرف کوتاه مدت برق کشور با استفاده از شبکه های عصبی و تبدیل موجک (با تاکید بر اثرات محیطی و اقلیمی)"
در زیر چکیده این مقاله را مشاهده می کنید:

 

"مدل سازی و پیش بینی مصرف کوتاه مدت برق کشور با استفاده از شبکه های عصبی و تبدیل موجک (با تاکید بر اثرات محیطی و اقلیمی)"

 چکیده - امروزه انرژی الکتریکی به عنوان یکی از مهم ترین بخش های انرژی کشور ضمن داشتن نقش موثر در تولید و مصرف، اهمیت ویژه ای در فرایند تصمیم گیری های اقتصادی دارد. آگاهی از میزان تقاضای انرژی الکتریکی در هر دوره به منظور برنامه ریزه دقیق، جهت اعمال سیاست گذاری های لازم، امری ضروری می باشد. از این رو ...
کلید واژه: پیش بینی، تقاضای برق، تبدیل موجک، شبکه های عصبی شعاع مدار، ARIMA

لینک دانلود: 13.pdf


سه شنبه 12 اسفند 1393 15:10 توسط آلینداس با موضوع اخبار تخصصی
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