Short-term electric load forecasting based on a neural fuzzy network

Centre for Multimedia Signal Processing, Department of Electronic and Information Engineering2003-2004 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Short-Term Electric Load Forecasting Based on a Neural Fuzzy Network

Electric load forecasting is essential to improve the reliability of the ac power line data network and provide optimal load scheduling in an intelligent home system. In this paper, a short-term load forecasting realized by a neural fuzzy network (NFN) and a modified genetic algorithm (GA) is proposed. It can forecast the hourly load accurately with respect to different day types and weather information. By introducing new genetic operators, the modified GA performs better than the traditional GA under some benchmark test functions. The optimal network structure can be found by the modified GA when switches in the links of the network are introduced. The membership functions and the number of rules of the NFN can be obtained automatically. Results for a short-term load forecasting will be given

Optimal and stable fuzzy controllers for nonlinear systems based on an improved genetic algorithm

Centre for Multimedia Signal Processing, Department of Electronic and Information Engineering2003-2004 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Tuning of the structure and parameters of a neural network using an improved genetic algorithm

This paper presents the tuning of the structure and parameters of a neural network using an improved genetic algorithm (GA). It will also be shown that the improved GA performs better than the standard GA based on some benchmark test functions. A neural network with switches introduced to its links is proposed. By doing this, the proposed neural network can learn both the input-output relationships of an application and the network structure using the improved GA. The number of hidden nodes is chosen manually by increasing it from a small number until the learning performance in terms of fitness value is good enough. Application examples on sunspot forecasting and associative memory are given to show the merits of the improved GA and the proposed neural network

A novel genetic-algorithm-based neural network for short-term load forecasting

This paper presents a neural network with a novel neuron model. In this model, the neuron has two activation functions and exhibits a node-to-node relationship in the hidden layer. This neural network provides better performance than a traditional feedforward neural network, and fewer hidden nodes are needed. The parameters of the proposed neural network are tuned by a genetic algorithm with arithmetic crossover and nonuniform mutation. Some applications are given to show the merits of the proposed neural network

Gain estimation for an AC power line data network transmitter using a neural-fuzzy network and an improved genetic algorithm

Author name used in this publication: F. H. F. LeungAuthor name used in this publication: P. K. S. TamAuthor name used in this publication: Y. S. LeeCentre for Multimedia Signal Processing, Department of Electronic and Information EngineeringRefereed conference paper2002-2003 > Academic research: refereed > Refereed conference paperVersion of RecordPublishe

A novel genetic-algorithm-based neural network for short-term load forecasting

Centre for Multimedia Signal Processing, Department of Electronic and Information Engineering2003-2004 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

MRI-based radiomics for prognosis of pediatric diffuse intrinsic pontine glioma: an international study.

Background: Diffuse intrinsic pontine gliomas (DIPGs) are lethal pediatric brain tumors. Presently, MRI is the mainstay of disease diagnosis and surveillance. We identify clinically significant computational features from MRI and create a prognostic machine learning model. Methods: We isolated tumor volumes of T1-post-contrast (T1) and T2-weighted (T2) MRIs from 177 treatment-naïve DIPG patients from an international cohort for model training and testing. The Quantitative Image Feature Pipeline and PyRadiomics was used for feature extraction. Ten-fold cross-validation of least absolute shrinkage and selection operator Cox regression selected optimal features to predict overall survival in the training dataset and tested in the independent testing dataset. We analyzed model performance using clinical variables (age at diagnosis and sex) only, radiomics only, and radiomics plus clinical variables. Results: All selected features were intensity and texture-based on the wavelet-filtered images (3 T1 gray-level co-occurrence matrix (GLCM) texture features, T2 GLCM texture feature, and T2 first-order mean). This multivariable Cox model demonstrated a concordance of 0.68 (95% CI: 0.61-0.74) in the training dataset, significantly outperforming the clinical-only model (C = 0.57 [95% CI: 0.49-0.64]). Adding clinical features to radiomics slightly improved performance (C = 0.70 [95% CI: 0.64-0.77]). The combined radiomics and clinical model was validated in the independent testing dataset (C = 0.59 [95% CI: 0.51-0.67], Noether's test P = .02). Conclusions: In this international study, we demonstrate the use of radiomic signatures to create a machine learning model for DIPG prognostication. Standardized, quantitative approaches that objectively measure DIPG changes, including computational MRI evaluation, could offer new approaches to assessing tumor phenotype and serve a future role for optimizing clinical trial eligibility and tumor surveillance