Hybrid credit ranking intelligent system using expert system and artificial neural networks

2009 ◽  
Vol 34 (1) ◽  
pp. 28-46 ◽  
Author(s):  
Arash Bahrammirzaee ◽  
Ali Rajabzadeh Ghatari ◽  
Parviz Ahmadi ◽  
Kurosh Madani
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Expert System ◽  
Intelligent System ◽  
Artificial Neural

Expert system for heart function based on artificial neural networks and fuzzy theory

1998 ◽  
Author(s):  
Wei Yu ◽  
Xiaoying Li ◽  
Daoyin Yu ◽  
Yi Mao ◽  
Qi Hua
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Expert System ◽  
Fuzzy Theory ◽  
Heart Function ◽  
Artificial Neural

A New Intelligent System Methodology for Time Series Forecasting with Artificial Neural Networks

2008 ◽  
Vol 28 (2) ◽  
pp. 113-129 ◽  
Author(s):  
Tiago A. E. Ferreira ◽  
Germano C. Vasconcelos ◽  
Paulo J. L. Adeodato
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Intelligent System ◽  
Time Series Forecasting ◽  
Artificial Neural ◽  
System Methodology

scholarly journals THE INTELLIGENT SYSTEM FOR AUTOMOTIVE FUELS QUALITY DEFINITION

2013 ◽  
Vol 3 (3) ◽  
pp. 11-13
Author(s):  
Volodymyr Drevetskiy ◽  
Marko Klepach
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Diesel Fuel ◽  
Octane Number ◽  
Intelligent System ◽  
Cetane Number ◽  
Cetane Index ◽  
Hydrodynamic Method ◽  
Artificial Neural ◽  
Automotive Fuels

An intelligent system, based on hydrodynamic method and artificial neural networks usage for automotive fuels quality definition have been developed. Artificial neural networks optimal structures for the octane number of gasoline, cetane number, cetane index of diesel fuel definition have been substantiated and their accuracy has been analyzed. The implementation of artificial neural networks by means of microcontroller-based systems has been considered.

Autonomous Design of Artificial Neural Networks by Neurex

1996 ◽  
Vol 8 (8) ◽  
pp. 1767-1786 ◽  
Author(s):  
François Michaud ◽  
Ruben Gonzalez Rubio
Keyword(s):  
Neural Networks ◽  
Mathematical Model ◽  
Artificial Neural Networks ◽  
Expert System ◽  
Design Process ◽  
Design Rules ◽  
Learning Abilities ◽  
Artificial Neural ◽  
Number Of Iterations ◽  
Learning Law

Artificial neural networks (ANN) have been demonstrated to be increasingly more useful for complex problems difficult to solve with conventional methods. With their learning abilities, they avoid having to develop a mathematical model or acquiring the appropriate knowledge to solve a task. The difficulty now lies in the ANN design process. A lot of choices must be made to design an ANN, and there are no available design rules to make these choices directly for a particular problem. Therefore, the design of an ANN demands a certain number of iterations, mainly guided by the expertise and the intuition of the developer. To automate the ANN design process, we have developed Neurex, composed of an expert system and an ANN simulator. Neurex autonomously guides the iterative ANN design process. Its structure tries to reproduce the design steps done by a human expert in conceiving an ANN. As a whole, the Neurex structure serves as a framework to implement this expertise for different learning paradigms. This article presents the system's general characteristics and its use in designing ANN using the standard backpropagation learning law.

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