On the construction and training of reformulated radial basis function neural networks

Karayiannis, N.B.; Randolph-Gips, Mary M.

doi:10.1109/tnn.2003.813841

Cited by 113 publications

(68 citation statements)

References 17 publications

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“…The regularization parameter λ occurs in (30) because the ROLS solution for weights is used [48]. The concept of LOO cross validation, as well as the derivation of (29) and (30), are detailed in Appendix B.…”

Section: B Regression Model Constructionmentioning

confidence: 99%

Particle Swarm Optimization Aided Orthogonal Forward Regression for Unified Data Modeling

Chen

Hong

Harris

2010

IEEE Trans. Evol. Computat.

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Abstract-We propose a unified data modeling approach that is equally applicable to supervised regression and classification applications, as well as to unsupervised probability density function estimation. A particle swarm optimization (PSO) aided orthogonal forward regression (OFR) algorithm based on leaveone-out (LOO) criteria is developed to construct parsimonious radial basis function (RBF) networks with tunable nodes. Each stage of the construction process determines the center vector and diagonal covariance matrix of one RBF node by minimizing the LOO statistics. For regression applications, the LOO criterion is chosen to be the LOO mean square error, while the LOO misclassification rate is adopted in two-class classification applications. By adopting the Parzen window estimate as the desired response, the unsupervised density estimation problem is transformed into a constrained regression problem. This PSO aided OFR algorithm for tunable-node RBF networks is capable of constructing very parsimonious RBF models that generalize well, and our analysis and experimental results demonstrate that the algorithm is computationally even simpler than the efficient regularization assisted orthogonal least square algorithm based on LOO criteria for selecting fixed-node RBF models. Another significant advantage of the proposed learning procedure is that it does not have learning hyperparameters that have to be tuned using costly cross validation. The effectiveness of the proposed PSO aided OFR construction procedure is illustrated using several examples taken from regression and classification, as well as density estimation applications.

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Section: B Regression Model Constructionmentioning

confidence: 99%

Particle Swarm Optimization Aided Orthogonal Forward Regression for Unified Data Modeling

Chen

Hong

Harris

2010

IEEE Trans. Evol. Computat.

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“…Thus, accurate wind forecasts are of primary importance to solve operational, planning, and economic problems in the growing wind power scenario [4,5]. Current wind power forecasting research has been divided into point forecasts (also called deterministic predictions) [6][7][8] and uncertainty forecasts [9,10]. Deterministic forecasts deliver specific amounts of wind power and focus on reducing the forecasting error [11].…”

Section: Introductionmentioning

confidence: 99%

Comparison Study of AI-based Methods in Wind Energy

Jiang¹,

Liu²,

Song³

2016

New Applications of Artificial Intelligence

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Wind energy forecasting is particularly important for wind farms due to cost-related issues, dispatch planning, and energy market operations. Thus, improving forecasting accuracy becomes an urgent task for researchers in the field of wind energy. However, there is limited research to discuss an overall comparison among various forecasting types, which is a foundation for future works with respect to wind energy prediction because this comparison may reveal whether there is a best model for a specific forecasting type or specific data in this field. For the purpose of laying a strong foundation for wind energy research, this chapter introduces five basic forecasting models, which are Autoregressive Moving Average Model (ARMA), Back-Propagation Neuron Network (BPNN), Support Vector Regression (SVR), Extreme Learning Machine (ELM), and Adaptive Network-Based Fuzzy Inference System (ANFIS) with implement codes before comparing the forecasting effectiveness of five different models in three wind farms based on five forecasting types. Comparison results indicate that each model has great divergent forecasting results in different wind farms and every forecasting type has its own "best model."

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“…Furthermore, for the mechanical press, unknown high impact loading exists in the stamping stage, an intelligent control approach should be used to compensate this disturbance. Many researches about fuzzy control (FC), [15][16][17] neural network control (NNC), [18][19][20] and iterative learning control (ILC) 21 have been studied for adaptive compensators and dynamic observers. The radial basis function neural network (RBFNN), which has simple structure and fast convergence speed, [22][23][24] is one of the most effective intelligent compensation methods.…”

Section: Introductionmentioning

confidence: 99%

Hybrid-loop servo control system of double toggle mechanical press for flexible forming process based on sliding mode control and neural network techniques

Liang

Zhao

et al. 2015

Proceedings of the Institution of Mechanical Engineers, Part I:

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A hybrid-loop servo control system based on sliding-mode control and neural network control is proposed in this article to realize flexible forming process on a double toggle mechanical press. First, kinematics and dynamic analysis of the drive system are conducted to derive the control objective behavior. Then, the structure of the hybrid-loop system is introduced; sliding-mode control is applied to compensate the punch position errors. In the inner loop of the servo motor, complementary sliding-mode control is used to track the motor rotation angle, and a radial basis function neural network estimator is applied to eliminate the considerable load disturbances in the stamping stage. Finally, experimental hardware is constructed and a compound blanking and drawing process is carried out to validate the proposed servo control system in practice. With different materials and drawing depth, high tracking accuracy and robustness are exhibited to realize favorable forming performance.

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On the construction and training of reformulated radial basis function neural networks

Cited by 113 publications

References 17 publications

Particle Swarm Optimization Aided Orthogonal Forward Regression for Unified Data Modeling

Particle Swarm Optimization Aided Orthogonal Forward Regression for Unified Data Modeling

Comparison Study of AI-based Methods in Wind Energy

Hybrid-loop servo control system of double toggle mechanical press for flexible forming process based on sliding mode control and neural network techniques

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