Membership Training

Views Updated: Dec 15, 2025

Abbreviation

Pronunciation

/ˈmɛmbərˌʃɪp ˈtreɪnɪŋ/

The effect that the combination of input, the number of membership function of input, training times and training sample affect the learning ability and generalization ability of NFS was researched respectively. The relation between the learning ability and generalization ability was studied.
This algorithm is realized by means of obtaining membership function, constructing and training fuzzy neural network, then pruning network and extracting rules.
In addition we discuss the influence upon training efficiency and performance of this network generated by overlapping state of membership functions of fuzzy subsets. We bring forward modifying parameters of membership functions during samples learning and training. The problem on selecting and optimizing parameters was resolved effectively.
Basically, it is a competitive soft learning vector quantization embedded with reinforcement learning, and the reinforcement signal is so constructed as to depend on the membership function of the supervised signal of the corresponding training vector.
Considering long time delay and strong coupling existed in the process of continuous carbonation decomposition, an intelligent resolution ratio control system is developed. In this system, the membership functions and fuzzy reference rules are optimized by genetic algorithm through training the stable error as objective function.

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