in this paper, a novel hybrid model based on neural network and game theory is proposed to support the analyzers in oil market. in this model, first the neural network is utilized to learn the oil prices associated with opec production level and usa imports level. then the learned neural network is applied by a game model. finally the nash equilibrium points of the game present the optimum deci...

cognitive architectures (cas) are the core of artificial cognitive systems. a ca is supposed to specify the human brain at a level of abstraction suitable for explaining how it achieves the functions of the mind. over the years a number of distinct cas have been proposed by different authors and their limitations and potentials were investigated. these cas are usually classified as symbolic and...

there is different methods for simulating river flow. some of thesemethods such as the process based hydrological models need multiple input data and high expertise about the hydrologic process. but some of the methods such as the regression based and artificial inteligens modelsare applicable even in data scarce conditions. this capability can improve efficiency of the hydrologic modeling in u...

Introduction: Identifying the potential firing patterns following different brain regions under normal and abnormal conditions increases our understanding of events at the level of neural interactions in the brain. Furthermore, it is important to be capable of modeling the potential neural activities to build precise artificial neural networks. The Izhikevich model is one of the simplest biolog...

( ) ABSTRACT: Artificial neural networks ANN recently gained attention as a fast and flexible vehicle to microwave modeling and design. Fast neural models trained from measured simulated microwave data can be used during microwave design to provide instant answers to the task they have learned. We review two important aspects of neural-network-based microwave modeling, namely, model development...

This paper attempts to employ and investigate neural based approaches as interpolation tools for modeling of Switched Reluctance Motor (SRM) drive. Precise modeling of SRM is essential to analyse the performance of control strategies for variable speed drive application. In this work the suitability of Generalized Regression Neural Network (GRNN) and Extreme Learning Machine (ELM) in addition t...