Support Vector Machine Weather Prediction Technology Based on the Improved Quantum Optimization Algorithm.

Jinlei Zhang,Yumin Dong,Xue Qiu,Xiang Li,Zhijie Huang,Mingqiu Wu,Daniele Bibbo

doi:10.1155/2021/6653659

Jinlei Zhang, Yumin Dong + Show 5 more

Open Access

https://doi.org/10.1155/2021/6653659

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Abstract

Emotion recognition is a research hotspot in the field of artificial intelligence. If the human-computer interaction system can sense human emotion and express emotion, it will make the interaction between the robot and human more natural. In this paper, a multimodal emotion recognition model based on many-objective optimization algorithm is proposed for the first time. The model integrates voice information and facial information and can simultaneously optimize the accuracy and uniformity of recognition. This paper compares the emotion recognition algorithm based on many-objective algorithm optimization with the single-modal emotion recognition model proposed in this paper and the ISMS_ALA model proposed by recent related research. The experimental results show that compared with the single-mode emotion recognition, the proposed model has a great improvement in each evaluation index. At the same time, the accuracy of emotion recognition is 2.88% higher than that of the ISMS_ALA model. The experimental results show that the many-objective optimization algorithm can effectively improve the performance of the multimodal emotion recognition model.

Highlights

It is worth mentioning that rainfall is the only input element source for the hydrologic cycle
Based on the above analysis, a new weather prediction model based on the improved quantum genetic algorithm (IQGA) [22] and support vector machines (SVMs) [23,24,25] is proposed to solve the problems in short-term and medium-range weather prediction
The real complex weather dataset is selected as the experimental data through ingenious data preprocessing to enhance the potential of data mining. en, the traditional quantum genetic algorithm is improved from several directions which effectively enhances the global search ability and efficiency of the quantum genetic algorithm

Summary

Introduction

It is worth mentioning that rainfall is the only input element source for the hydrologic cycle. Based on the above analysis, a new weather prediction model based on the improved quantum genetic algorithm (IQGA) [22] and support vector machines (SVMs) [23,24,25] is proposed to solve the problems in short-term and medium-range weather prediction. Because many parameters will affect the efficiency of the algorithms at the same time, it is very inefficient to use traditional grid search, learning curve, and other parameter adjustment methods It will take a lot of time and often cannot achieve the optimal classification effect. E improved quantum genetic algorithm can be applied to the parameter optimization of other machine learning algorithms to increase the calculation efficiency and the optimization accuracy. It can be used in various optimization problems to improve the search efficiency

Related Technology Introduction

IQGA-SVM Model

Simulation

Findings

Conclusion