A5101625390- Machine Learning and ELM
- Advanced SAR Imaging Techniques
- MicroRNA in disease regulation
- Wireless Signal Modulation Classification
- Machine Learning and Algorithms
- Metaheuristic Optimization Algorithms Research
- Microwave Imaging and Scattering Analysis
- Gait Recognition and Analysis
- Optical Systems and Laser Technology
- Traffic control and management
- Transportation Planning and Optimization
- Traffic Prediction and Management Techniques
Jinan University
2019
National University of Defense Technology
2017-2018
Accurate and stable short-term traffic flow prediction is an indispensable part in current intelligent transportation systems. In this paper, a novel forecasting model termed as EnLSTM-WPEO proposed based on ensemble learning of long short term memory neural network (LSTM), no negative constraint theory (NNCT) weight integration population extremal optimization (PEO) algorithm. the first stage, cluster LSTMs constructed to separately forecast with different time lag, which significant...
A novel radar high-resolution range profile (HRRP) target recognition method based on a stacked autoencoder (SAE) and extreme learning machine (ELM) is presented in this paper. As key component of deep structure, the SAE does not only learn features by making use data, it also obtains feature expressions at different levels data. However, with hard to achieve good generalization performance fast speed. ELM, as new algorithm for single hidden layer feedforward neural networks (SLFNs), has...
A novel evolutionary extreme learning machine (ELM) based on improved quantum‐behaved particle swarm optimization (IQPSO) for radar target classification is presented in this paper. Quantum‐behaved (QPSO) has been used ELM to solve the problem that needs more hidden nodes than conventional tuning‐based algorithms due random set of input weights and biases. But method calculating characteristic length Delta potential well QPSO may reduce global search ability algorithm. To issue, a new...
Radar target classification is very important in military and civilian fields. Extreme Learning Machines (ELMs) are widely used because of their fast learning speed good generalization performance. However, shallow architecture, ELMs may not effectively capture the data high level abstractions. Although many researchers have proposed Deep Machine (DELM), which can be to automatically learn feature representations, model easily falls into overfitting when training sample limited. To address...