An Incremental Regularization Kernel Randomized Neural Network for Electrical Energy Output Prediction in Combined Cycle Power Plant

Incremental randomized neural networks have been widely applied in industrial data modeling. However, incremental randomized neural networks may generate redundant hidden nodes. These nodes may lead to weak performance in real world data modeling tasks. An important factor is that the neural nodes constructed in the early stages may influence the quality of subsequently generated nodes. To resolve this drawback, an incremental regularization kernel randomized neural network (IRKRNN) is proposed in this work. IRKRNN adopts a kernel learning method to enhance the feature expression during the parameter-learning. Meanwhile, the uncertainty of random mapping is reduced. Each randomly generated node is projected into kernel space. When new nodes are generated, the kernel space is constantly updated. Moreover, the number of nodes and the expected tolerance are used as criteria for terminating the kernel-based incremental learning process to achieve a compact network structure. Finally, IRKRNN is compared with state-of-the-art randomized neural networks on six real world regression datasets and the electrical energy output prediction task of the combined cycle power plant. Experimental results indicate that the proposed IRKRNN achieves satisfactory generalization performance, and it has good potential for industrial data modeling tasks.