Forecasting by Combining Chaotic PSO and Automated LSSVR
نویسندگان
چکیده
An automatic least square support vector regression (LSSVR) optimization method that uses mixed kernel chaotic particle swarm (CPSO) to handle issues has been provided. The LSSVR model is composed of three components. position the particles (solution) in a sequence with good randomness and ergodicity initial characteristics taken into consideration first section. binary (PSO) used choose potential input characteristic combinations makes up second final step involves using search narrow down set before combining PSO-optimized parameters create CP-LSSVR. CP-LSSVR forecast impressive datasets testing targets obtained from UCI dataset for purposes illustration evaluation. results suggest predictive capability discussed this paper can build projected utilizing limited number characteristics.
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ژورنال
عنوان ژورنال: Technologies (Basel)
سال: 2023
ISSN: ['2227-7080']
DOI: https://doi.org/10.3390/technologies11020050