Fig. 1. Experimental device for data acquisition of underwater bubbles; (b) Photo of the underwater lidar system (inside the metal box); (c) The laser runs through the bubbles without background light; (d) Air pump (voltage: 220 V, frequency: 50 Hz, power: 60 W, transmission volume: 50 L/min); (e) Bubbles plate (the diameter of the air bubbles is about 10-200 μm); (f) Valve: control airflow

Fig. 2. Echo signals of bubbles

Fig. 3. Score distribution of the first two principal components

Fig. 4. Iterative convergence of different algorithms. (a) Elastic BP algorithm; (b) Adaptive and additional momentum BP algorithm

Fig. 5. Underwater bubbles recognition process based on PCA and elastic BP neural network

Fig. 6. Echo signals and recognition results under different conditions. (a) Different targets echo curves; (b) Low density bubbles recognition rate; (c) High density bubbles recognition rate

Table 1. System parameters

Table 2. Training of different hidden nodes

Table 3. Recognition results under different cumulative contribution rates

Table 4. Recognition and contrast results of different algorithms