Fuzzy connectives based crossover operators to model genetic algorithms population diversity
Fuzzy Sets and Systems
A learning process for fuzzy control rules using genetic algorithms
Fuzzy Sets and Systems
Fuzzy modeling with hybrid systems
Fuzzy Sets and Systems
Fuzzy Control of HVAC Systems Optimized by Genetic Algorithms
Applied Intelligence
Engineering Applications of Artificial Intelligence
A 2-tuple fuzzy linguistic representation model for computing with words
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
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The use of knowledge-based systems can represent an efficient approach for system management, providing automatic control strategies with Artificial Intelligence capabilities. By means of Artificial Intelligence, the system is capable of assessing, diagnosing and suggesting the best operation mode. One important Artificial Intelligence tool for automatic control is the use of fuzzy logic controllers, which are fuzzy rule-based systems comprising the expert knowledge in form of linguistic rules. These rules are usually constructed by an expert in the field of interest who can link the facts with conclusions. However, this way to work sometimes fails to obtain an optimal behavior. To solve this problem, within the framework of Machine Learning, some artificial intelligence techniques could be applied to enhance the controller behavior. In this work, a post-processing method is used to obtain more compact and accurate fuzzy logic controllers. This method combines a new technique to perform an evolutionary lateral tuning of the linguistic variables with a simple technique for rule selection (that removes unnecessary rules). To do so, the tuning technique considers a new rule representation scheme by using the linguistic 2-tuples representation model which allows the lateral variation of the involved linguistic labels.