Application of Broad Learning System in Discrimination of Mushroom Toxicity
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Abstract:
In order to improve the accuracy of mushroom toxicity discrimination and eliminate individual differences, a method of mushroom toxicity discrimination based on broad learning system was proposed in this work. Firstly, the correlation between each characteristic of mushroom and its toxicity was explored. The results showed that the odor and color of mushroom were the most distinguishing characteristics. These results were consistent with the experience of manual discrimination. Then, broad learning system was established and trained. By comparing performance in diverse sample sizes of different methods, it was found that when the sample size is larger than 1000, the classification accuracy of the broad learning system was higher than 99.5%. Compared with BP-neural network, the proposed method was of high-accuracy and fast-training. Finally, according to the incremental learning algorithm of broad learning system, when the performance of the system does not meet the requirements, the system can be updated quickly by increasing the hidden nodes, and the accuracy can be improved from 98.55% to 99.99% without retraining the whole network. It was possible to discriminate the toxicity of mushrooms in real time. Therefore, compared with other methods, this method of mushroom toxicity discrimination based on broad learning system has the advantages of high accuracy, short training time, rapid discrimination and easy expansion.
