Large-scale systems: modeling, control, and fuzzy logic
Large-scale systems: modeling, control, and fuzzy logic
Soft computing for control of non-linear dynamical systems
Soft computing for control of non-linear dynamical systems
Soft Computing and Its Applications
Soft Computing and Its Applications
Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing)
Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing)
IEEE Transactions on Neural Networks
Neuro-fuzzy rule generation: survey in soft computing framework
IEEE Transactions on Neural Networks
Optimization of Ni-MH Battery Fast Charging in Electric Vehicles Using Dynamic Data Mining and ANFIS
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence
CASE'09 Proceedings of the fifth annual IEEE international conference on Automation science and engineering
Data mining via rules extracted from GMDH: an application to predict churn in bank credit cards
KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part I
A hybrid system for dental milling parameters optimisation
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part II
Review: Data mining techniques and applications - A decade review from 2000 to 2011
Expert Systems with Applications: An International Journal
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Battery charging controllers design and application is a growing industry direction. Fast and efficient charging of battery packs is a problem which is difficult and often expensive to solve using conventional techniques. The majority of existing works on intelligent charging systems are based on expert knowledge and heuristics. Not all features of the desired charging behavior can be attained by the hard-wired logic implemented by expert generated rules. Because the battery charging is a highly dynamic process and the chemical technology a battery uses varies significantly for different battery types, data mining technique can be of real importance for extracting the charging rules from the large databases, especially when the charging logic is to be continuously changed during the life of the battery dependent on the type and characteristics of the battery and utilization conditions. In this paper we use soft computing-based data mining technique for extraction of control rules for effective and fast battery charging process. The obtained rules were used for NiCd battery charging. The comparative performance evaluation was done among the existing charging control methods and the proposed system, which demonstrated a significant increase of performance (minimum charging time and minimum overheating) using the soft computing-based approach.