Design patterns: elements of reusable object-oriented software
Design patterns: elements of reusable object-oriented software
VLSI-Compatible Immplementations for Artificial Neural Networks
VLSI-Compatible Immplementations for Artificial Neural Networks
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
A MDA based SoC Modeling Approach using UML and SystemC
CIT '06 Proceedings of the Sixth IEEE International Conference on Computer and Information Technology
Reconfigurable Parallel Hardware for Computing Local Linear Neuro-Fuzzy Model
PARELEC '06 Proceedings of the international symposium on Parallel Computing in Electrical Engineering
Applying brain emotional learning algorithm for multivariable control of HVAC systems
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Implementation issues of neuro-fuzzy hardware: going toward HW/SW codesign
IEEE Transactions on Neural Networks
Attention to multiple local critics in decision making and control
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
Implementation of intelligent and bio-inspired algorithms in industrial and real applications is arduous, time consuming and costly; in addition, many aspects of system from high level behavior of algorithm to energy consumption of targeted system must be considered simultaneously in the design process. Advancement of hardware platforms such as DSPs, FPGAs and ASICs in recent years has made it increasingly possible to implement computationally complex intelligent systems; on the other hand, however, the design and testing costs of these systems are high. Reusability and extendibility features of the developed models can decrease the total cost and time-to-market of an intelligent system. In this work, model driven development approach is utilized for implementation of emotional learning as a bio-inspired algorithm for embedded purposes. Recent studies show that emotion is a mechanism for fast decision making in human and other animals, and can be assumed as an expert system. Mathematical models have been developed for describing emotion in mammals from cognitive studies. Here brain emotional based learning intelligent controller (BELBIC), which is based on mammalian middle brain, is designed and implemented on FPGA and the obtained embedded emotional controller (E-BELBIC) is utilized for controlling real laboratorial overhead traveling crane in model-free and embedded manner. Short time-to-market, easy testing and error handling, separating concerns, improving reusability and extendibility of obtained models in similar applications are some benefits of the model driven development methodology.