The integration of neural networks and fuzzy inference systems could be formulated into
three main categories: cooperative, concurrent and integrated neuro-fuzzy models. We …

Problem solving is the process of finding a solution when the path leading to that solution is
uncertain. Even though we are familiar with several problem-solving techniques, in the real …

We propose a hybrid algorithm of two fuzzy genetics-based machine learning approaches
(ie, Michigan and Pittsburgh) for designing fuzzy rule-based classification systems. First, we …

Tuning fuzzy rule-based systems for linguistic fuzzy modeling is an interesting and widely
developed task. It involves adjusting some of the components of the knowledge base without …

Fuzzy Systems have shown their utility for solving a wide range of problems in different
application domains. The use of Genetic Algorithms for designing Fuzzy Systems allows us …

In this paper, an evolutionary fuzzy neural network using fuzzy logic, neural networks (NNs),
and genetic algorithms (GAs) is proposed for financial prediction with hybrid input data sets …

Logic rules and membership functions are two key components of a fuzzy logic controller
(FLC). If only one component is learned, the other one is often set subjectively thus can …

Online adaptive temperature control by field-programmable gate array (FPGA)-implemented
adaptive recurrent fuzzy controller (ARFC) chip is proposed in this paper. The RFC is …

Abstract A Genetic Algorithm (GA) based fuzzy gain scheduling approach for load frequency
control is proposed in this paper. In this approach, a fuzzy system is used to adaptively …

In this work we propose an immune-based approach for designing of fuzzy systems. From
numerical data and with membership function previously defined, the immune algorithm …