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Evolutionary neuro-fuzzy control
2017This chapter focuses on the use of genetic algorithms (GAs) in the design of FLC. An approach of adopting genetic algorithm search is adopted to determine optimal FLC scaling factors. The approach is then extended by adoption of neural network learning of the scaling factors leading to a neuro-fuzzy control method. This is further combined with genetic
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Evolutionary-Neuro-Fuzzy Control
2013It has been demonstrated that learning the shape of sigmoidal function can improve performance of neuro-fuzzy controller. Backpropagation learning algorithm does not include the parameter of the sigmoidal function shape. This chapter proposes the use of genetic algorithm to learn the weights, biases and shape of the sigmoidal function of the neural ...
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Fuzzy and Neuro-fuzzy Techniques for Modelling and Control
2006This paper presents comparative evaluation of two fuzzy-derived techniques for modelling fuel spray penetration in the cylinder of a diesel internal combustion engine and a fuzzy control system for a small internal combustion engine. The first technique used a pure fuzzy paradigm, the parameters of this technique are a collection of intuitively ...
Shaun H. Lee +2 more
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Behavior-based neuro-fuzzy controller for mobile robot navigation
IEEE Transactions on Instrumentation and Measurement, 2003E M Petriu +2 more
exaly
dSPACE based adaptive neuro-fuzzy controller of grid interactive inverter
Energy Conversion and Management, 2012Necmi Altin, Ibrahim Sefa
exaly
Neuro-fuzzy controller for control and robotics applications
Engineering Applications of Artificial Intelligence, 1994D H Rao, M M Gupta
exaly
Optimal Neuro-Fuzzy External Controller for a STATCOM in the 12-Bus Benchmark Power System
IEEE Transactions on Power Delivery, 2007Salman Mohagheghi +2 more
exaly