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>Classification capability of OPNN and APNN with five types of feature inputs with 4- and 5-types of overlapped patterns—without clustering algorithm (in the histogram dotted chequered blocks refer to 4 and 5 type inputs to PNN; striped and brick blocks refer to 4- and 5-type inputs to APNN).</table>

Journal of Electrical and Computer Engineering

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Figure 16: 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 

Figure 16 | 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 