Titlebook: Intelligent Control; A Hybrid Approach Ba Nazmul Siddique Book 2014 Springer International Publishing Switzerland 2014 Evolutionary Algorit

facetious

Evolutionary-Fuzzy Control,n increasing number of inputs and linguistic variables, the possible number of rules for the system increases exponentially, which makes it difficult for experts to define a complete set of rules and associated MFs. This chapter investigates the use of genetic algorithm (GA) for an automated way of

冷淡周邊

Neuro-Fuzzy Control, used in such adjustment procedure, which involves several parameters of the MFs to be adjusted. In many cases, tuning the scaling factors gives the same performance as with MFs adjustment. Secondly, tuning the scaling factors is a simpler task than adjusting the membership functions. This chapter d

Peculate

Evolutionary-Neuro-Fuzzy Control,rithm does notinclude the parameter of the sigmoidal function shape. This chapter proposes the useof genetic algorithm to learn the weights, biases and shape of the sigmoidal functionof the neural network simultaneously. The performance of the system using a neuralnetwork with a linear activation fu

相互影響

成份

exceed

Using Technology to Teach a New Generation,ss of individual students. This allows for the early identification of students that are not keeping up with the course and allows for early intervention in such cases. Overall this leads to a better course with higher throughput and better student retention.