A hierarchical approach to multi-class fuzzy classifiers

Classification rule hierarchical tuning with linguistic modification based on solving fuzzy relational equations

“…Hierarchical tuning requires the definition of conditions for modification of the primary rules, as well as the selection of the modified candidate rules [4,5]. For this purpose, models of linguistic modifiers are developed.…”

Hybrid fuzzy-PID control of a nuclear Cyber-Physical System working under varying environmental conditions

“…By so doing, the hybrid fuzzy-PID is capable of controlling the CPS under varying environmental conditions. To increase the transparency of the hybrid fuzzy-PID control, the fuzzy rules are here optimized by resorting to a grid-type fuzzy partitioning approach [24], [26]- [29], where the fuzzy sets and the fuzzy rules are learnt from examples of input variables and optimal control settings.…”

“…To show the effect of the environmental variable The rules of the FLC are learnt by resorting to a grid-type fuzzy partitioning approach, as illustrated in the next subsection [24], [26]- [29], [32]. fuzzy partitioning approach can be used to learn the fuzzy rules from the numerical data of the sampled input-output data pairs [26], [29], [32]. The objective of the grid-type fuzzy partitioning approach is to separate the input-output feature space into a set of uniform or non-uniform grids with predefined membership functions, and, then, to obtain the most transparent fuzzy rules linking the antecedents and consequents of the available examples.…”

A Review of Classification Problems and Algorithms in Renewable Energy Applications

“…Accuracy values over 97% were reported with this technique in experiments over the Indian subcontinent, both in cloud detection over land and sea cases. On the other hand, in [74], a hierarchical approach for classification based on fuzzy rules was proposed. The main parameters of the proposed method were optimized by means of a GA, conforming a genetic fuzzy system.…”

“…Problem Specific Methodology Used [52] 2015 Power disturbance Classification SVM [53] 2013 Power disturbance Classification DT, ANN, neuro-fuzzy, SVM [54] 2014 Power disturbance Classification DT, SVM [55] 2014 Power disturbance Classification DT [56] 2012 Power disturbance Classification DT, DE [57] 2004 Power disturbance Classification Fuzzy expert, ANN [58] 2010 Power disturbance Classification Fuzzy classifiers [59] 2010 Power disturbance Classification GFS [48] 2006 Appliance load monitoring Classification ANN [60] 2009 Appliance load monitoring Classification k-NN, DTs, naive Bayes [61] 2010 Appliance load monitoring Classification k-NN, DTs, naive Bayes [62,63] 2010 Appliance load monitoring Classification LR, ANN [64] 2012 Appliance load monitoring Classification SVM [65] 2013 Solar Classification, regression SVM, ANN, ANFIS, wavelet, GA [66] 2008 Solar Classification, regression ANN, fuzzy systems, meta-heuristics [67] 2004 Solar Classification PNN [68] 2006 Solar Classification PNN [69] 2009 Solar Classification PNN, SOM, SVM [70] 2004 Solar Classification SVM [71] 2014 Solar Classification SVM [72] 2015 Solar Classification SVM [73] 2006 Solar Classification Fuzzy rules [74] 2013 Solar Classification Fuzzy classifiers [75] 2014 Solar Classification Fuzzy rules…”