Potential Application of Neural Networks to Verification and Validation of Complex Systems

1993 ◽  
pp. 617-623
Author(s):  
Ozer Ciftcioglu ◽  
Erdinc Turkcan
Keyword(s):  
Neural Networks ◽  
Complex Systems ◽  
Potential Application ◽  
Verification And Validation

Modularity in Artificial Neural Networks

2011 ◽  
pp. 1095-1101
Author(s):  
Ricardo Téllez ◽  
Cecilio Angulo
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Complex Systems ◽  
Intelligent Systems ◽  
Point Of View ◽  
Main Concern ◽  
Complex Structures ◽  
Computational Point ◽  
Points Of View

The concept of modularity is a main concern for the generation of artificially intelligent systems. Modularity is an ubiquitous organization principle found everywhere in natural and artificial complex systems (Callebaut, 2005). Evidences from biological and philosophical points of view (Caelli and Wen, 1999) (Fodor, 1983), indicate that modularity is a requisite for complex intelligent behaviour. Besides, from an engineering point of view, modularity seems to be the only way for the construction of complex structures. Hence, whether complex neural programs for complex agents are desired, modularity is required. This article introduces the concepts of modularity and module from a computational point of view, and how they apply to the generation of neural programs based on modules. Two levels, strategic and tactical, at which modularity can be implemented, are identified. How they work and how they can be combined for the generation of a completely modular controller for a neural network based agent is presented.

scholarly journals Recurrent Trainable Neural Networks for Complex Systems Identification: A Hybrid System Approach

2016 ◽  
Vol 118 (1) ◽  
pp. 97-106
Author(s):  
Juan-Eduardo Velázquez-Velázquez ◽  
Rosalba Galván-Guerra ◽  
Ieroham Baruch ◽  
Silvestre Garcia-Sanchez
Keyword(s):  
Neural Networks ◽  
Complex Systems ◽  
Hybrid System ◽  
System Approach ◽  
Systems Identification

scholarly journals TOWARDS EVOLUTIONARY DESIGN OF COMPLEX SYSTEMS INSPIRED BY NATURE

2014 ◽  
Vol 54 (5) ◽  
pp. 367-377
Author(s):  
Jaroslav Vítku ◽  
Pavel Nahodil
Keyword(s):  
Neural Networks ◽  
Complex Systems ◽  
Human Brain ◽  
Evolutionary Algorithm ◽  
Autonomous Systems ◽  
Hybrid Networks ◽  
Evolutionary Design ◽  
Agent Architecture ◽  
Simple Experiment ◽  
Agent Architectures

This paper presents first steps towards evolutionary design of complex autonomous systems. The approach is inspired in modularity of human brain and principles of evolution. Rather than evolving neural networks or neural-based systems, the approach focuses on evolving hybrid networks composed of heterogeneous sub-systems implementing various algorithms/behaviors. Currently, the evolutionary techniques are used to optimize weights between predefined blocks (so called Neural Modules) in order to find an agent architecture appropriate for given task. The framework, together with the simulator of such systems is presented. Then, examples of agent architectures represented as hybrid networks are presented. One architecture is hand-designed and one is automatically optimized by means of evolutionary algorithm. Even on such a simple experiment, it can be observed how the evolution is able to pick-up unexpected attributes of the task and exploit them when designing new architecture.

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