Optimized Artificial Neural network models to time series

تعد الشبكات العصبية الاصطناعية أداة قوية وفعالة في تطبيقات السلاسل الزمنية. ان الهدف الأول في هذا البحث هو تشخيص أفضل واكفئ نماذج الشبكات العصبية الاصطناعي (الانتشار العكسي، دالة الأساس الشعاعي، الشبكة التكرارية) غفي حل السلاسل الزمنية الخطية وغير الخطية. اما الهدف الثاني هو التخلص من مشاكل الامثلية المحلية التي تعد من اهم مشاكل تقدير النماذج غير الخطية، واختبار حصانة نماذج الشبكات العصبية الاصطناعية. من اجل تشخيص افضل او امثل نماذج الشبكات العصبية في هذا البحث، استعمل مهارة التنبؤ لقياس كفاءة أداء نماذج الشبكات العصبية الاصطناعية. كما تم استعمال جذر معدل مربع الخطأ والمتوسط المطلق للخطأ النسبي لقياس دقة خطأ الطرائق المعتمدة. ان اهم ما تم التوصل اليه من خلال هذا البحث هو الشبكة العصبية الاصطناعية الأمثل كانت شبكة الانتشار العكسي والشبكة التكرارية لحل السلاسل الزمنية سواء كانت الخطية او غير الخطية. اثبت النتائج عدم كفاءة ودفة شبكة دالة الأساس الشعاعي لمعالجة السلاسل الزمنية غير الخطية، وكفاءتها في السلاسل الزمنية الخطية او شبه الخطية فقط، وعدم مقدرتها على التخلص من مشاكل الامثلية المحلية. النتائج المقدمة في هذا البحث تحسن من طرائق الحديثة لمعالجة التنبؤ بالسلاسل الزمنية.        Artificial Neural networks (ANN) are powerful and effective tools in time-series applications. The first aim of this paper is to diagnose better and more efficient ANN models (Back Propagation, Radial Basis Function Neural networks (RBF), and Recurrent neural networks) in solving the linear and nonlinear time-series behavior. The second aim is dealing with finding accurate estimators as the convergence sometimes is stack in the local minima. It is one of the problems that can bias the test of the robustness of the ANN in time series forecasting. To determine the best or the optimal ANN models, forecast Skill (SS) employed to measure the efficiency of the performance of ANN models. The mean square error and the absolute mean square error were also used to measure the accuracy of the estimation for methods used. The important result obtained in this paper is that the optimal neural network was the Backpropagation (BP) and Recurrent neural networks (RNN) to solve time series, whether linear, semilinear, or non-linear. Besides, the result proved that the inefficiency and inaccuracy (failure) of RBF in solving nonlinear time series. However, RBF shows good efficiency in the case of linear or semi-linear time series only. It overcomes the problem of local minimum. The results showed improvements in the modern methods for time series forecasting

Neural networks and MIMD-multiprocessors

Two artificial neural network models are compared. They are the Hopfield Neural Network Model and the Sparse Distributed Memory model. Distributed algorithms for both of them are designed and implemented. The run time characteristics of the algorithms are analyzed theoretically and tested in practice. The storage capacities of the networks are compared. Implementations are done using a distributed multiprocessor system

Multilayered feed forward Artificial Neural Network model to predict the average summer-monsoon rainfall in India

In the present research, possibility of predicting average summer-monsoon rainfall over India has been analyzed through Artificial Neural Network models. In formulating the Artificial Neural Network based predictive model, three layered networks have been constructed with sigmoid non-linearity. The models under study are different in the number of hidden neurons. After a thorough training and test procedure, neural net with three nodes in the hidden layer is found to be the best predictive model.Comment: 19 pages, 1 table, 3 figure

Artificial neural networks as models of stimulus control

We evaluate the ability of artificial neural network models (multi-layer perceptrons) to predict stimulus-­response relationships. A variety of empirical results are considered, such as generalization, peak-shift (supernormality) and stimulus intensity effects. The networks were trained on the same tasks as the animals in the considered experiments. The subsequent generalization tests on the networks showed that the model replicates correctly the empirical results. It is concluded that these models are valuable tools in the study of animal behaviour

Neural Network Models for Inflation Forecasting: An Appraisal

We assess the power of artificial neural network models as forecasting tools for monthly inflation rates for 28 OECD countries. For short out-of-sample forecasting horizons, we find that, on average, for 45% of the countries the ANN models were a superior predictor while the AR1 model performed better for 21%. Furthermore, arithmetic combinations of several ANN models can also serve as a credible tool for forecasting inflation.Artificial Neural Networks; Forecasting; Inflation