Effectiveness of Partition and Graph Theoretic Clustering Algorithms for Multiple Source Partial Discharge Pattern Classification Using Probabilistic Neural Network and Its Adaptive Version: A Critique Based on Experimental Studies

Table 6

Comparison of classification capability of OPNN and APNN with LVQ versions’ clustering algorithms.

Input feature vector	Classification capability
	LVQ1 clustering				LVQ2 clustering						LVQ3 clustering
	OPNN		APNN		OPNN			APNN			OPNN				APNN
	Iter.	(%)	Iter.	(%)	Iter.	ε	(%)	Iter.	ε	(%)	Iter.	ε	η	(%)	Iter.	ε	η	(%)
Φ-𝑞max-𝑛 (30°)	5000	89	5000	91	500	.5	95	1000	.5	94	1000	.6	.3	93	1000	.7	.3	93
Φ-𝑞max-𝑛 (10°)	5000	87	5000	88	1000	.6	90	1000	.7	93	1000	.8	.2	89	1000	.7	.2	89
Φ-𝑞min-𝑛 (30°)	1000	91	1000	93	1000	.6	94	1000	.8	93	5000	.7	.3	92	1000	.6	.3	93
Φ-𝑞min-𝑛 (10°)	1000	88	1000	89	1000	.7	91	1000	.7	93	1000	.7	.3	91	1000	.8	.3	92
Measure of types of mean (30°)	1000	91	1000	93	1000	.8	95	1000	.8	96	1000	.7	.3	92	3000	.7	.3	93
Measure of types of mean (10°)	1000	92	1000	93	1000	.8	95	1000	.8	96	1000	.7	.3	92	1000	.8	.3	94