Small neural networks with a constrained number of trainable parameters, can be suitable resource-efficient candidates for many simple tasks, where now excessively large models are used. However, such face several problems during the learning process, mainly due to redundancy individual neurons, which results in sub-optimal accuracy or need additional training steps. Here, we explore diversity ...

on one hand, oil is the greatest energy resource in the world and, on the other hand, because of the role of oil revenue in the economic of oil producer countries, such as iran,it is vital for these countries. so it is necessary to recognize different affective parameters on oil market for these countries. in this research, we try to forecast oil price as an important variable in world wide oil...

Production of highly viscous tar sand bitumen using Steam Assisted Gravity Drainage (SAGD) with a pair of horizontal wells has advantages over conventional steam flooding. This paper explores the use of Artificial Neural Networks (ANNs) as an alternative to the traditional SAGD simulation approach. Feed forward, multi-layered neural network meta-models are trained through the Back-...

in this study, a low-cost, rapid and qualitative evaluation procedure is presented using dynamic pattern recognition analysis to assess liquefaction potential which is useful in the planning, zoning, general hazard assessment, and delineation of areas, dynamic pattern recognition using neural networks is generally considered to be an effective tool for assessing of hazard potential on the basis...

Economic Dispatch Problem (EDP) has been discussed with reference to the developments based on Artificial Neural Networks (ANN) approaches. A selected survey / overview on Economic Dispatch using Artificial Neural Network within the IEE/IEEE publications frame work have been presented.

Dynamic networks are used in a wide range of fields, including social network analysis, recommender systems, and epidemiology. Representing complex as structures changing over time allow models to leverage not only structural but also temporal patterns. However, dynamic literature stems from diverse fields makes use inconsistent terminology, it is challenging navigate. Meanwhile, graph neural (...

the stability of learning rate in neural network identifiers and controllers is one of the challenging issues which attracts great interest from researchers of neural networks. this paper suggests adaptive gradient descent algorithm with stable learning laws for modified dynamic neural network (mdnn) and studies the stability of this algorithm. also, stable learning algorithm for parameters of ...

Objective (s): Artificial Neural Networks (ANN) are widely used for predicting systems’ behavior. GMDH is a type of ANNs which has remarkable ability in pattern recognition. The aim the current study is proposing a model to predict dynamic viscosity of silver/water nanofluid which can be used as antimicrobial fluid in several medical purposes.Materials and Methods: In order to have precise mode...