Investigation of RBFNN Based on Improved PSO Optimization Algorithm for Performance and Emissions Prediction of a High‐Pressure Common‐Rail Diesel Engine

Abstract

The purpose of this study is improve calibration efficiency and obtain accurate diesel engine operating parameters, achieving improved diesel engine emissions and fuel efficiency. A PSO‐RBF (particle swarm optimization‐radial basis function) diesel engine performance prediction model combining an improved PSO (particle swarm optimization algorithm and an RBF neural network is proposed. A space‐filling experimental design method for diesel engine performance prediction is proposed based on the actual operating conditions of diesel engines. Training data are collected at the bench to build the RBF prediction model. An optimization PSO search method is proposed to improve the PSO optimization capability. An improved PSO algorithm is used to optimize the model and improve prediction accuracy. Then the BSFC (diesel brake‐specific fuel consumption), NOx ((Nitrogen Oxid), CO (Carbon Monoxide), and HC (Hydrocarbon) prediction models are constructed. Results show that the PSO‐RBF can find the global solution with good prediction accuracy and generalization ability during small amounts of data. The PSO‐RBF model fitting degrees of BSFC, NOx, CO, and HC are 0.9952, 0.9910, 0.9820, and 0.9870 respectively. Mean relative errors are 3.02%, 2.78%, 1.39%, and 2.01% respectively. Mean absolute percentage errors are 1.58%, 3.26%, 3.69%, and 2.96% respectively. The optimized model R2 (Model determination coefficient) is improved by 0.065, 0.102, 0.10, and 0.085, respectively.

Chinese medicine based on word embedding Deberta-BiLSTM-CRF model named entity recognition

“…In this paper, we employ a DeBERTa-based named entity recognition model, which consists of an embedding layer, an encoding layer, and an output layer [3] and the model structure is shown in Figure 1. The embedding layer encodes character and vocabulary information into char embeddings and soft lexicon embeddings respectively, and concatenates them.…”

“…The BERT (Bidirectional Encoder Representations from Transformers) pre-training model proposed by Devlin [2] and other scholars uses Transformer as the main architecture and uses positional encoding to understand the order of language [3]. Through the self-attention mechanism, BERT can better capture the contextual relationship between words in the text, so as to better represent the semantic information of the text [1].…”

Dynamic Evacuation Routes Navigation for Passenger Ship Fire Based on Intelligence Algorithms

“…In recent years, many researchers have explored using artificial intelligence algorithms with promising results. [21][22][23][24] Zhai et al [25] proposed a new evacuation route planning method combining genetic and simulated annealing algorithms. Guo et al [26] used the improved particle swarm optimization algorithm to achieve the global path planning of unmanned surface vehicles.…”