نتایج جستجو برای: narx
تعداد نتایج: 507 فیلتر نتایج به سال:
It has recently been shown that gradient descent learning algorithms for recurrent neural networks can perform poorly on tasks that involve long{term dependencies, i.e. those problems for which the desired output depends on inputs presented at times far in the past. In this paper we explore the long{term dependencies problem for a class of architectures called NARX recurrent neural networks, wh...
برنامه CE-QUAL-W2 یک مدل فیزیکی با اطمینانپذیری بالا جهت شبیهسازی هیدرودینامیکی-کیفی مخازن بوده که هزینه محاسباتی زیادی دارد. بنابراین یافتن مدلهای جایگزین که نتایج این مدل را با دقت مطلوب و در زمان اندکی برآورد کنند از اهمیت کاربردی بالایی برخوردار است. در این تحقیق قابلیت مدل شبکه عصبی NARX به عنوان مدل جایگزین CE-QUAL-W2 جهت پیشبینی نتایج بلند مدت شوری خروجی از مخزن بررسی شده است. برای ا...
Recurrent neural networks have become popular models for system identiication and time series prediction. NARX (Nonlinear AutoRegressive models with eXogenous inputs) neural network models are a popular subclass of recurrent networks and have been used in many applications. Though embedded memory can be found in all recurrent network models, it is particularly prominent in NARX models. We show ...
Chaotic time-series is a dynamic nonlinear system whose features can not be fully reflected by Linear Regression Model or Static Neural Network. While Nonlinear Autoregressive with eXogenous input includes feedback of network output, therefore, it can better reflect the system’s dynamic feature. Take annual active times of sunspot as an example, after verifying the chaos of sunspot time-series ...
A neural network based-approach for structural health monitoring was presented. The proposed approach involves two steps. The first step, system identification, uses NARX (Non-linear Auto-Regressive with eXogenous) neural networks to identify the undamaged and damaged states of a structural system. The second step, structural damage detection, uses the aforementioned trained NARX neural network...
Recurrent neural networks have become popular models for system identification and time series prediction. Nonlinear autoregressive models with exogenous inputs (NARX) neural network models are a popular subclass of recurrent networks and have been used in many applications. Although embedded memory can be found in all recurrent network models, it is particularly prominent in NARX models. We sh...
It has previously been shown that gradient-descent learning algorithms for recurrent neural networks can perform poorly on tasks that involve long-term dependencies, i.e. those problems for which the desired output depends on inputs presented at times far in the past. We show that the long-term dependencies problem is lessened for a class of architectures called nonlinear autoregressive models ...
It has recently been shown that gradient-descent learning algorithms for recurrent neural networks can perform poorly on tasks that involve long-term dependencies, i.e., those problems for which the desired output depends on inputs presented at times far in the past. We show that the long-term dependencies problem is lessened for a class of architectures called Nonlinear AutoRegressive models w...
This paper investigates the technique of the modeling and identification a new dynamic NARX fuzzy model by means of genetic algorithms. In conventional identification techniques, difficulties such as poor knowledge of the process, inaccurate process or complexity of the resulting mathematical model, all which limit their usefulness during dealing with dynamic nonlinear industrial processes. To ...
As renewable energy increasingly integrates into the electric power system, electric load forecasting and renewable energy power generation forecasting become more important. In this project, ARIMA and NARX are applied to build load forecasting model focusing on improving statistical and computational efficiency without losing accuracy. ARIMA turns out to be better for short term forecasting wh...
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