The Essential Order of (L_p,p

درسنا في هذا البحث درجة التقريب الاساسي بأستخدام الشبكة العصبية المنتظمة ، وكيف يمكن تقريب الدوال  المتعددة المتغيرات في فضاء  عندما  بأستخدام الشبكة العصبية الامامية المنتظمة ، وكذلك بامكاننا الحصول على مبرهنات مباشرة وعكسية ونظرية تكافؤ للتقريب المتعددة المتغيرات في فضاء  عندما  بأستخدام الشبكة العصبية الامامية المنتظمة .This paper is concerning with essential degree of approximation using regular neural networks and how a multivariate function in  spaces for  can be approximated using a forward regular neural network. So, we can have the essential approximation ability of a multivariate function in  spaces for  using regular FFN

A Novel Progressive Multi-label Classifier for Classincremental Data

In this paper, a progressive learning algorithm for multi-label classification to learn new labels while retaining the knowledge of previous labels is designed. New output neurons corresponding to new labels are added and the neural network connections and parameters are automatically restructured as if the label has been introduced from the beginning. This work is the first of the kind in multi-label classifier for class-incremental learning. It is useful for real-world applications such as robotics where streaming data are available and the number of labels is often unknown. Based on the Extreme Learning Machine framework, a novel universal classifier with plug and play capabilities for progressive multi-label classification is developed. Experimental results on various benchmark synthetic and real datasets validate the efficiency and effectiveness of our proposed algorithm.Comment: 5 pages, 3 figures, 4 table

Application of new adaptive higher order neural networks in data mining

This paper introduces an adaptive Higher Order Neural Network (HONN) model and applies it in data mining such as simulating and forecasting government taxation revenues. The proposed adaptive HONN model offers significant advantages over conventional Artificial Neural Network (ANN) models such as much reduced network size, faster training, as well as much improved simulation and forecasting errors. The generalization ability of this HONN model is explored and discussed. A new approach for determining the best number of hidden neurons is also proposed