Design a PID Controller for Suspension System by Back Propagation Neural Network

Table 5

The (𝑅²) values for PID gain parameters with various neurons in the hidden layer.

Number of; hidden neuron	Acronym of training method
	LM	BFG	RP	SCG	CGB	CGF	CGP	OSS	GDX
7	0.9977	0.9888	0.9819	0.9957	0.9738	0.9918	0.9905	0.9913	0.9891
8	0.9981	0.9676	0.992	0.9909	0.9942	0.9864	0.9846	0.9969	0.9916
10	0.9999	0.9981	0.9942	0.9578	0.9986	0.9978	0.9681	0.9963	0.9945
15	0.9985	0.9998	0.9967	0.9996	0.9996	0.9995	0.9595	0.9890	0.9856
18	0.9967	0.9928	0.9994	0.9998	0.9998	0.9698	0.9898	0.9898	0.9881
20	0.9998	0.9499	0.999	0.9699	0.9997	0.9698	0.9699	0.9797	0.9950
25	0.9996	0.9699	0.9994	0.9699	0.9699	0.9599	0.9899	0.9479	0.9983
26	0.9944	0.9916	0.9996	0.9797	0.9694	0.9792	0.9799	0.9688	0.9782
27	0.9989	0.9599	0.9995	0.9899	0.9899	0.9899	0.9799	0.9896	0.9870
28	0.9998	0.9939	0.9994	0.9799	0.9999	0.9599	0.9799	0.9889	0.9984
29	0.9997	0.9419	0.9997	0.9799	0.9599	0.9699	0.9799	0.9889	0.9985