Fast stochastic configuration network based on an improved sparrow search algorithm for fire flame recognition

• Published in: Knowledge-based systems Vol. 245; p. 108626
• Main Authors: Wu, Hao, Zhang, Aihua, Han, Ying, Nan, Juan, Li, Kun
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 07.06.2022; Elsevier Science Ltd
• Subjects: Algorithms; Color space; Configuration management; Experiments; Feature extraction; Fire detection; Fire flame recognition; Image classification; Image processing; Mutation; Nodes; Object recognition; Optimization; Parameters; Search algorithms; Sine map; Sparrow search algorithm; Stochastic configuration network; Verification; Fire flame recognition; Sparrow search algorithm; Sine map; Stochastic configuration network; Optimization; Color space
• ISSN: 0950-7051; 1872-7409
• DOI: 10.1016/j.knosys.2022.108626

Flame image recognition is of great significance in the fire detection and prevention. In this paper, in order to improve the accuracy of fire recognition, a fast stochastic configuration network (FSCN) method based on an improved sparrow search algorithm (ISSA) is proposed. In the design of fast stochastic configuration network (FSCN), the gradual increase of hidden layer nodes in the original stochastic configuration network (SCN) is canceled, and the number of them is set directly. An improved sparrow search algorithm (ISSA) is used to generate the input weights and biases of hidden layer nodes. At the same time, the supervisory mechanism is retained to judge the weights and biases of all hidden layer nodes, and ISSA is used to regenerate corresponding weights and biases for the nodes that do not meet the constraints in the supervisory mechanism. In the ISSA, sine map, adaptive adjustment of hyper-parameters and mutation strategy are used to improve the optimization ability of the original sparrow search algorithm (SSA). Some parameters in FSCN are optimized by ISSA to make it have better classification performance. Finally, the image processing technology is used to extract features from the flame images and the interference images, and then the feature vectors are obtained to train the ISSA-FSCN. Several simulation experiments have been carried out to verify effectiveness of the proposed ISSA-FSCN method. In the performance verification of ISSA on CEC test suit, ISSA averagely outperforms other algorithms by 33.6% in the average results of 20 functions. In the performance verification of FSCN, the average results of accuracy, precision, recall, F1 and auc are compared. In the experiment 1, ISSA-FSCN averagely outperforms other algorithms by 19.7%, 14.7%, 12.8%, 14.5% and 23.0%. In the experiment 2, ISSA-FSCN averagely outperforms other algorithms by 2.3%, 2.1%, 2.5%, 6.0%, 3.2%. In the experiment 3, ISSA-FSCN averagely outperforms other algorithms by 5.9%, 4.0%, 4.1%, 4.1%, 6.8%. •A fast stochastic configuration network (FSCN) was proposed.•An improved sparrow search algorithm was proposed with good optimization ability.•The proposed ISSA-FSCN was applied to the classification of flame images.