A Machine Learning Based 3D Propagation Model for Intelligent Future Cellular Networksdoi:10.1109/GLOBECOM38437.2019.9014187Usama MasoodHasan FarooqAli Imran
Fan Q, Fan T (2021) A hybrid model of extreme learning machine based on bat and cuckoo search algorithm for regression and multiclass classification. J Math Haykin S (2004) N. network: a comprehensive foundation. Neural Netw 2:41 Google Scholar Huang BG, Zhu QY, Siew CK (2006) Extreme...
For a machine learning model to generalize well, one needs to ensure that its decisions are supported by meaningful patterns in the input data. A prerequisite is however for the model to be able to explain itself, e.g. by highlighting which input features it uses to support its prediction....
reinforcement-learningneural-networksbayesian-inferenceprobabilistic-graphical-modelsbayesian-filterbelief-propagationcomputational-psychiatrystate-space-modeljaxgraph-neural-networkspredictive-codingactive-inferencehierarchical-gaussian-filter UpdatedJan 27, 2025
Measurements were carried out over a distance to determine various received power levels from a fixed WLAN access pointtransmitter; these values were applied to some path loss model equations to obtain the mobile radio planning parameters such as the path loss exponent, the mean path loss intercept...
py --dims=[3,3,27,3] --depths=[96,192,384,768] --dp_rate=0.1 --model_dir='checkpoints/Small.pth' # SPNet-Base python test.py --dims=[3,3,27,3] --depths=[128,256,512,1024] --dp_rate=0.1 --model_dir='checkpoints/Base.pth' # SPNet-Large python test.py --dims=[3,...
We introduce different model-free reinforcement-learning techniques, unitedly called Sparse Cooperative Q-learning, which approximate the global action-value function based on the topology of a coordination graph, and perform updates using the contribution of the individual agents to the maximal global ...
(DBPNN),based on back propagation algorithm and time series model is presented.The DBPNN can be used to model dynamic nonlinear systems.Furthermore,with the special structure of the integrated node layer,the complexity of the DBPNN is largely reduced.By using data from laboratory model and ...
Combustion machine learning: Principles, progress and prospects: Combustion machine learning 2022, Progress in Energy and Combustion Science Show abstract Probabilistic Wildfire Segmentation Using Supervised Deep Generative Model from Satellite Imagery 2023, Remote Sensing A review of machine learning application...
A Bayesian Optimized Discriminant Analysis Model for Condition Monitoring of Face Milling Cutter Using Vibration Datasets 2022, Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems Machine Learning Based Quantitative Damage Monitoring of Composite Structure 2022, International Jo...