Building electrical energy consumption forecasting analysis using conventional and artificial intelligence methods: A review
It is important for building owners and operators to manage the electrical energy
consumption of their buildings. As electrical energy is the major form of energy consumed in …
consumption of their buildings. As electrical energy is the major form of energy consumed in …
Neural networks: An overview of early research, current frameworks and new challenges
This paper presents a comprehensive overview of modelling, simulation and implementation
of neural networks, taking into account that two aims have emerged in this area: the …
of neural networks, taking into account that two aims have emerged in this area: the …
A particle swarm optimization-based flexible convolutional autoencoder for image classification
Convolutional autoencoders (CAEs) have shown their remarkable performance in stacking
to deep convolutional neural networks (CNNs) for classifying image data during the past …
to deep convolutional neural networks (CNNs) for classifying image data during the past …
Recent advances in particle swarm
Recent advances in particle swarm Page 1 Recent Advances in Particle Swarm Xiaohui Hu
Yuhui Shi Russ Eberhart Department of Biomedical Engineering EDS Embeded Systems …
Yuhui Shi Russ Eberhart Department of Biomedical Engineering EDS Embeded Systems …
Artificial intelligence techniques: an introduction to their use for modelling environmental systems
SH Chen, AJ Jakeman, JP Norton - Mathematics and computers in …, 2008 - Elsevier
Knowledge-based or Artificial Intelligence techniques are used increasingly as alternatives
to more classical techniques to model environmental systems. We review some of them and …
to more classical techniques to model environmental systems. We review some of them and …
Hybrid interpretable predictive machine learning model for air pollution prediction
Air pollution prediction is a burning issue, as pollutants can harm human health. Traditional
machine learning models usually aim to improve the overall prediction accuracy but neglect …
machine learning models usually aim to improve the overall prediction accuracy but neglect …
Memristor devices for neural networks
H Jeong, L Shi - Journal of Physics D: Applied Physics, 2018 - iopscience.iop.org
Neural network technologies have taken center stage owing to their powerful computing
capability for supporting deep learning in artificial intelligence. However, conventional …
capability for supporting deep learning in artificial intelligence. However, conventional …
An improved PSO-based ANN with simulated annealing technique
Y Da, G Xiurun - Neurocomputing, 2005 - Elsevier
This paper presents a modified particle swarm optimization (PSO) with simulated annealing
(SA) technique. An improved PSO-based artificial neural network (ANN) is developed. The …
(SA) technique. An improved PSO-based artificial neural network (ANN) is developed. The …
Evolving artificial neural networks using an improved PSO and DPSO
This paper presents an improved particle swarm optimization (PSO) and discrete PSO
(DPSO) with an enhancement operation by using a self-adaptive evolution strategies (ES) …
(DPSO) with an enhancement operation by using a self-adaptive evolution strategies (ES) …
A fuzzy adaptive turbulent particle swarm optimisation
Particle Swarm Optimisation (PSO) algorithm is a stochastic search technique, which has
exhibited good performance across a wide range of applications. However, very often for …
exhibited good performance across a wide range of applications. However, very often for …